Week 2026-W17

Research Engineer focused on Reinforcement Learning to advance capabilities and safety of large language models. This role involves implementing novel approaches, contributing to research direction, and creating agentic models for tasks like computer use and autonomous software generation, while also improving reasoning abilities and developing prototypes. Key responsibilities include architecting RL infrastructure, designing training environments and methodologies, driving performance improvements, and collaborating across teams.

Research Engineer focused on Reinforcement Learning to advance capabilities and safety of large language models. This role involves implementing novel approaches, contributing to research direction, creating agentic models via tool use for tasks like computer use and autonomous software generation, and improving reasoning abilities. Projects include architecting RL infrastructure, designing training environments and evaluations for RL agents, driving performance improvements, and developing automated testing frameworks.

Research Engineer/Scientist focused on pre-training large language models, with an emphasis on multimodal capabilities. The role involves research, implementation, experimentation, and optimization of training infrastructure and model architectures, contributing to the development of safe and steerable AI systems.

Research Engineer/Scientist focused on pre-training large language models, involving research in model architecture, algorithms, data processing, and optimizer development, as well as optimizing and scaling training infrastructure.

Research Scientist focused on mechanistic interpretability of LLMs, aiming to understand how trained models work by reverse-engineering their parameters and algorithms. The role involves developing methods, designing experiments, creating interpretability features, building infrastructure, and collaborating with other teams. Requires strong scientific research background with some interpretability work, comfort with experimental science, and proficiency in Python.

This is a research fellowship program focused on Reinforcement Learning (RL) within AI safety. Fellows will work on empirical projects, potentially using external infrastructure, with the goal of producing public outputs like paper submissions. The program emphasizes mentorship from Anthropic researchers and provides a stipend and compute funding. Key activities include building model-based tools for data quality, understanding generalization, and creating RL environments for capabilities and safety tasks.

Applied AI Engineer role focused on advising and partnering with AI-native startups to build on the Claude Developer Platform. Responsibilities include technical guidance, developing evaluation frameworks, designing scalable architectures, and creating technical resources to help startups succeed with Claude. Requires production experience with LLM-powered applications, agent architectures, and evaluation frameworks.

Research Scientist/Engineer focused on honesty in language models, developing techniques to minimize hallucinations and enhance truthfulness. This involves data curation, classifier development, evaluation frameworks, RAG implementation, human feedback collection, prompting pipelines, RL environments, and tools for human evaluators.

Machine Learning Systems Engineer focused on developing and optimizing encodings and tokenization systems for Anthropic's Finetuning workflows. This role acts as a bridge between Pretraining and Finetuning teams, building infrastructure crucial for model learning and data interpretation, impacting research progress and efficiency.

ML Systems Engineer focused on Reinforcement Learning Engineering to build, maintain, and improve the algorithms and infrastructure for training AI models like Claude using RLHF and other advanced techniques. The role emphasizes improving system performance, robustness, and usability to accelerate research breakthroughs in AI capabilities and safety.

Research Engineer role focused on applying reinforcement learning to cybersecurity tasks like secure coding and vulnerability remediation, blending research and engineering to train safe AI models. Requires cybersecurity domain expertise and ML/software engineering skills.

Research Engineer focused on building and improving the RL training infrastructure and tooling at Anthropic. The role involves identifying and removing bottlenecks in the RL stack, partnering with researchers and other engineering teams, and owning the reliability and performance of research runs to enable faster iteration and shipping of better models at scale.

The RL Velocity team owns the efficiency and reliability of the RL Science stack, building and improving the core platform for RL training runs to remove bottlenecks and enable faster iteration. This role focuses on ML infrastructure, distributed systems, and research tooling to improve the velocity and reliability of RL training at scale.

Research Engineer/Scientist focused on audio AI, working on training audio models, developing novel architectures, and optimizing inference for speech and audio understanding and generation systems.

Research Engineer/Scientist role focused on building large-scale ML systems, touching all parts of code and infrastructure, from cluster reliability and job efficiency to running scientific experiments and improving dev tooling. The role involves optimizing ML systems, comparing model variants, scaling training jobs, and designing fault tolerance strategies, with a focus on safe, steerable, and trustworthy AI.

Research Engineer/Scientist focused on vision and spatial reasoning for LLMs, working on pretraining, RL, and runtime techniques like agentic harnesses. Involves developing and evaluating multimodal capabilities, creating benchmarks, and partnering with product teams to improve Claude models.

Research Scientist focused on understanding and defending against societal risks from advanced AI models, particularly self-improving and autonomous systems. The role involves designing research experiments, building evals, and producing artifacts to communicate model capabilities and inform product/safeguards decisions. Emphasis on emerging risks from AI integration into the economy and society.

Senior Research Scientist focused on reward models for LLMs, involving novel architectures, RLHF, LLM-based evaluation, and mitigating reward hacking. Aims to improve model alignment with human values and translate research into production systems.

Applied AI Engineer role focused on being a technical advisor to customers adopting Claude LLMs. Responsibilities include guiding architecture design, developing evaluation frameworks, and advising on implementation patterns for LLMs via API. Requires production experience with LLMs, strong Python skills, and expertise in common LLM implementation patterns.

Applied AI Engineer role focused on being a technical advisor to customers adopting Anthropic's Claude LLM. Responsibilities include guiding customers through architecture design, developing evaluation frameworks, and implementing cutting-edge LLM patterns via API. Requires strong programming skills (Python) and production experience with LLMs, including agent development and evaluation.

Applied AI Engineer to serve as a technical advisor for companies building on the Claude Developer Platform, focusing on implementation, agent design, and building AI applications. Responsibilities include technical engagements, developing evaluation frameworks, designing architectures, and creating technical resources.

Applied AI Engineer focused on deploying AI to mission-driven organizations, advising on AI applications like evals and agent architectures, and building infrastructure to scale impact. Requires production experience with LLM applications and a builder mindset.

Applied AI Engineer role focused on deploying AI to mission-driven organizations, advising on evals and agent architectures, building ecosystem tooling, and prototyping new agents. Requires production experience with LLM applications and a builder mindset.

Applied AI Engineer role focused on being a technical advisor to enterprise clients adopting the Claude API. Responsibilities include advising on architecture, developing evaluation frameworks, guiding customers through LLM implementation patterns, and creating scalable assets. Requires production experience with LLMs, strong programming skills, and customer-facing experience.

Applied AI Engineer role focused on deploying Claude in life sciences to accelerate scientific progress. The role involves partnering with research institutions, building agents integrated into scientific workflows, and developing ecosystem infrastructure like MCP servers, benchmarks, and agent skills. The goal is to make Claude a go-to tool for the life sciences ecosystem, from discovery to pharma pipelines.

Research Scientist focused on biological safety for AI systems, applying technical skills to design and develop safety systems that detect harmful behaviors and prevent misuse. This role involves designing and executing capability evaluations, collaborating on training data and safety system training, analyzing performance, and stress-testing safeguards. The goal is to ensure biological safety is embedded throughout the model development lifecycle, balancing AI's potential in life sciences with preventing misuse.

Engineering Manager to lead the Cloud Inference team for AWS, responsible for scaling and optimizing Claude's inference, API, load balancing, capacity, and operations on AWS. The role ensures LLMs meet performance, safety, and security standards, and enhances global inference technology deployment. It focuses on increasing operational scale and accelerating the launch of new models and features.

Engineering Manager for Anthropic's performance and scaling teams, focusing on improving model performance and scaling inference and training systems. Responsibilities include front-line leadership, managing day-to-day execution, prioritizing work, and coaching reports. Requires management experience in technical environments, background in ML/AI, and interest in safe AI development.

Full-Stack Software Engineer to build platforms, tools, and interfaces for environment creation, data collection, and training observability for RL. The role involves owning product surfaces end-to-end, iterating on data collection strategies, and partnering with researchers to ship reliable products.

Research Scientist focused on analyzing real-world usage patterns of Claude, building evaluations to assess its behavior against its Constitution (safety, quality of advice), and partnering with fine-tuning, safeguards, policy, and interpretability teams to translate insights into model improvements. The role also involves generating insights on societal impacts to inform company strategy and policy, and sharing work through publications and presentations.

Senior Software Engineer on the Inference team responsible for building and maintaining systems that serve Claude models to millions of users. Focuses on maximizing compute efficiency and providing high-performance inference infrastructure for research.

Staff Software Engineer to build and operate safety, oversight, and intervention mechanisms for AI models (Claude) on third-party cloud service provider (CSP) platforms. This role ensures requests are monitored for misuse, enforced against policy, and compliant with data residency and privacy commitments. The engineer will integrate Safeguards into the CSP inference serving path, focusing on real-time enforcement, telemetry, and privacy architecture, while maintaining serving-path latency and scale. The work directly impacts the ability to ship frontier models on CSP platforms safely.

Software Engineer focused on building and maintaining the critical systems that serve Claude to millions of users worldwide. Responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators, maximizing compute efficiency and enabling research.

The Inference team at Anthropic is responsible for building and maintaining the systems that serve Claude to millions of users. This involves managing the entire stack from request routing to fleet-wide orchestration across diverse AI accelerators, with a dual mandate of maximizing compute efficiency and enabling research breakthroughs. The role requires significant software engineering experience, particularly with distributed systems, and experience with LLM inference optimization.

Staff Software Engineer on the Inference team responsible for building and maintaining systems that serve Claude to millions of users. Focuses on maximizing compute efficiency and enabling research through high-performance inference infrastructure, involving distributed systems, request routing, and LLM inference optimization.

Staff Software Engineer on the Inference team responsible for building and maintaining systems that serve Claude to millions of users. Focuses on maximizing compute efficiency and providing high-performance inference infrastructure for research, tackling complex distributed systems challenges across diverse AI accelerators.

Data Engineer for the Safeguards team, responsible for building data pipelines, warehousing solutions, and analytical tooling to support AI safety and trust efforts. The role focuses on data infrastructure for monitoring models, preventing misuse, and ensuring user well-being.

Engineering Manager for Anthropic's Inference Routing and Performance team, responsible for the cluster-level routing and coordination plane for the company's inference fleet. The role focuses on optimizing throughput and efficiency of AI model serving through custom algorithms, quantitative modeling, and deep systems understanding.

This role focuses on building and maintaining the infrastructure for deploying AI inference code to production across various accelerator fleets (GPU, TPU, Trainium). The core responsibility is to create a continuous, unattended deployment system that optimizes for resource constraints, minimizes cycle time, and ensures reliability at scale. It involves capacity-aware scheduling, deployment observability, and self-service onboarding for new models.

Staff + Sr. Software Engineer, Cloud Inference at Anthropic. This role focuses on scaling and optimizing Claude's inference across multiple cloud service providers (AWS, GCP, Azure). Responsibilities include designing and building serving infrastructure, collaborating with CSPs, developing CI/CD automation, creating abstraction layers for cost-effective inference management, capacity planning, and optimizing inference cost and performance. The role requires significant experience in large-scale distributed systems and cloud platforms, with a strong interest in inference.

Technical Program Manager focused on inference performance and efficiency for AI models, coordinating launches, managing dependencies, and optimizing runtime and accelerator performance across multiple hardware targets.

AI Engineer (Forward Deployed Engineer) at Databricks, focused on customer-facing GenAI solutions. This role involves building and productionizing AI applications using techniques like RAG, multi-agent systems, and fine-tuning, and deploying them on cloud platforms. The role requires strong data science experience and acting as a technical advisor to customers.

AI Engineer - Forward Deployed Engineer (FDE) role focused on delivering professional services to help customers build and productionize first-of-its-kind AI applications. This involves developing GenAI solutions, owning production rollouts, serving as a technical advisor, and collaborating with product/engineering teams. Requires experience building and deploying GenAI applications (RAG, multi-agent, fine-tuning) and production ML deployments.

AI Engineer - FDE (Forward Deployed Engineer) at Databricks, focused on building and productionizing GenAI solutions for customers, including RAG, multi-agent systems, and fine-tuning. The role involves serving as a technical advisor, owning production rollouts, and collaborating with product/engineering teams. Requires experience in GenAI application development, deployment, evaluation, and optimization, along with ML deployment on cloud platforms.

AI Engineer (Forward Deployed Engineer) for Databricks' federal sector team, focusing on building and productionizing GenAI applications for government customers. This role involves acting as a technical advisor, developing cutting-edge solutions using Mosaic AI research, and owning production rollouts of GenAI applications. Requires experience with RAG, multi-agent systems, fine-tuning, and deploying ML models on cloud platforms.

Research Scientist Intern role focused on domain adaptation for LLMs and AI systems in enterprise settings. Projects involve improving and evaluating literature methods, designing new methods, composing post-training techniques, and evaluating LLMs.

Lead a research team focused on advancing LLM training and inference efficiency, developing novel algorithms and systems for scaling, optimization, and adaptation. The role involves defining research roadmaps, driving innovations, and translating breakthroughs into production capabilities on the Databricks AI platform.

Lead a research team focused on advancing LLM training and inference efficiency, post-training optimization, and scaling. Drive algorithmic innovations and translate research into production capabilities for the Databricks AI platform.

Staff Software Engineer focused on the GenAI inference engine at Databricks, responsible for architecture, development, and optimization of high-throughput, low-latency LLM inference. This role involves kernel-level optimization, runtime development, orchestration, and integration with ML frameworks, bridging research advances with production demands.

AI Engineer (Forward Deployed Engineer) at Databricks focused on delivering professional services to help customers build and productionize GenAI applications. This role involves developing cutting-edge GenAI solutions, owning production rollouts, and serving as a technical advisor.

AI Engineer - Forward Deployed Engineer (FDE) at Databricks, focused on delivering professional services to help customers build and productionize GenAI applications. This role involves developing cutting-edge GenAI solutions, owning production rollouts, serving as a technical advisor, and collaborating with product/engineering teams. Requires experience building GenAI applications (RAG, multi-agent systems, Text2SQL, fine-tuning) and deploying production-grade GenAI applications.

AI Engineer (Forward Deployed Engineer) at Databricks focused on delivering professional services to help customers build and productionize GenAI applications. This role involves developing cutting-edge GenAI solutions, owning production rollouts, and serving as a technical advisor.

Senior Applied AI Engineer at Databricks focused on applying ML and optimization algorithms to improve user-facing products like AutoML, classification, regression, forecasting, and recommendations. The role involves building end-to-end systems, deploying ML/AI models, and architecting scalable ML infrastructure for training and serving. Experience with novel modeling techniques in ML for forecasting is a plus.

Hiring experienced machine learning platform engineers to build out a customer-facing generative AI platform for the ML development lifecycle, including data generation, training, evaluation, serving, and agent-building. The role involves end-to-end ownership, translating user requirements into product interfaces, and building backend distributed systems. Responsibilities span from user-facing features to low-level GPU orchestration.

Databricks is seeking a Senior Software Engineer for their AI Platform team in NYC. This role involves creating novel GenAI agent interfaces for complex workflows, leading full-stack development, and driving the strategy for end-to-end AI feature development. The ideal candidate has 5+ years of experience in web technologies, a strong product mindset, and hands-on experience with LLMs, RAG, prompt design, and production AI monitoring.

Senior Specialist Solutions Architect focusing on AI & ML Engineering for Databricks customers. The role involves architecting production-grade ML/AI workloads, including GenAI solutions like RAG and agents, optimizing training and inference, and applying MLOps best practices. It requires deep technical expertise in ML/AI, customer-facing skills, and collaboration with product and engineering teams to influence the roadmap.

Software Engineer focused on designing, developing, and optimizing the inference engine for Databricks' Foundation Model API. The role involves working on the full GenAI inference stack, including kernels, runtimes, orchestration, and memory management, to ensure fast, scalable, and efficient LLM serving systems.

Senior Developer Advocate focused on AI Agentic Systems, driving adoption and technical discourse for Databricks' AI offerings. Responsibilities include defining advocacy strategy, creating production-grade reference implementations, developing educational content, influencing product direction, and building the MLOps/LLMOps community. Requires deep technical expertise in Generative AI, MLOps, Python, ML frameworks, LLMOps orchestration tools, and the Databricks platform.

Databricks is seeking GenAI Engineers to drive the development and deployment of GenAI-powered products, focusing on enhancing LLM quality, expanding capabilities, and strengthening platform architecture. The role involves developing novel data collection, fine-tuning, and LLM technologies, designing ML pipelines, and building scalable backend systems for GenAI products.

Sr. Manager of Engineering to lead teams building the Databricks AI Gateway, an enterprise control plane for governing, routing, and monitoring LLM endpoints, coding agents, and model serving endpoints. The role involves launching and growing new products, focusing on standardizing, securing, and observing LLM inference traffic while managing cost, performance, and quality.

Databricks is seeking GenAI Engineers to drive the development and deployment of GenAI-powered products, focusing on enhancing LLM quality, expanding capabilities, and strengthening platform architecture. The role involves developing and implementing ML pipelines, building scalable backend systems, and working with cross-functional teams to deliver impactful AI solutions.

Staff Software Engineer for Databricks' AI Platform team in NYC, focusing on building new GenAI agent interfaces and end-to-end AI products from scratch in a 0-to-1 environment. Requires strong full-stack and generative AI experience, including RAG, prompt design, and production monitoring.

Staff Software Engineer focused on Machine Learning for Search at Databricks. The role involves developing and deploying ML-based search and discovery relevance models, enhancing search ranking, improving query understanding, building robust evaluations, and applying LLMs to search relevance. The goal is to enable seamless search at scale for Databricks customers.

Senior Applied AI Engineer focused on applying ML to improve Databricks' engineering systems and infrastructure, including cluster management and query compilation. The role involves building end-to-end systems, deploying models at scale, and architecting ML infrastructure for production environments.

Staff Backend Software Engineer for Databricks' AI Platform, focusing on the Model Serving product. The role involves designing and building scalable, low-latency inference systems for both CPU and GPU workloads, optimizing performance, and ensuring operational excellence. Key responsibilities include developing core serving infrastructure, driving architectural decisions, and collaborating across teams to deliver a world-class serving platform for enterprise AI/ML models.

Staff Backend Software Engineer for Databricks' AI Platform, focusing on Foundation Model Serving. The role involves designing and implementing high-throughput, low-latency inference systems for frontier AI models on GPU workloads, optimizing serving infrastructure, and influencing the technical roadmap for LLM APIs and runtimes at scale. Prior ML/AI experience is not required, but experience with large-scale distributed systems and operational sensitive systems is critical.

Staff Backend Software Engineer for Databricks' AI Platform team, focusing on building and improving the infrastructure that powers AI offerings like MLflow, AI Gateway, Agent Framework, and Foundation Model APIs. The role involves improving reliability, latency, and efficiency of distributed AI workloads and collaborating with various teams to deliver seamless end-to-end AI experiences.

Databricks is seeking a Staff Backend Software Engineer for their AI Platform team, focusing on the Model Serving product. The role involves designing and building systems for high-throughput, low-latency inference across CPU and GPU workloads, optimizing performance, and ensuring scalability and reliability. The engineer will contribute to core serving infrastructure, collaborate cross-functionally, and lead technical initiatives to improve latency, availability, and cost-effectiveness.

Staff Data Scientist focused on Trust and Safety at Databricks, developing and implementing ML models for fraud and abuse detection, analyzing security features, and collaborating with engineering and security teams to protect the platform and customers. The role involves creating compliance solutions, gathering requirements, and guiding junior team members.

Staff Product Manager for Databricks' AI Platform, focusing on driving the vision and roadmap for AI/ML infrastructure products that enable enterprises to build, train, deploy, and monitor AI systems. The role involves deep technical collaboration with engineering, customer engagement, and commercialization strategy for AI platform features, aiming to make it easier for enterprises to put AI into production.

Staff Product Manager for Databricks' AI Platform, focusing on the infrastructure that powers machine learning and AI at scale. The role drives the vision and roadmap for AI platform product areas, enabling enterprises to build, train, deploy, and monitor AI/ML systems. It involves deep technical collaboration, customer engagement, and commercialization strategy for AI platform features.

Director of AI Platforms responsible for building and leading a team that provides AI-enabling platform services, automation, and SDLC agents for SoFi. The role focuses on creating a shared foundation for teams to build, deploy, and operate AI capabilities through self-service workflows, standardized tooling, and clear operational contracts, while ensuring compliance with regulatory and governance requirements. Key responsibilities include defining the platform strategy and roadmap, driving adoption, managing vendor relationships, implementing governance and observability, and leading engineering teams.

Data Scientist at SoFi focused on developing and improving machine learning and statistical models for credit risk and operational areas. This role involves collaborating with various teams, leveraging data sources, and ensuring model rigor and monitoring.

Principal Product Manager to lead the strategy and roadmap for a Fraud, Risk & ML Platform. This platform is the unified foundation for event-driven decisioning, risk-based interdictions, real-time feature stores, and self-serve ML capabilities across the enterprise. The role focuses on reducing model time-to-market, enabling teams to build, deploy, and monitor ML models at enterprise scale, and improving platform adoption through a strong developer experience.

Seeking an experienced engineering leader to define the architecture and technical strategy for a customer-facing AI agent for financial guidance, leading its implementation and ensuring quality, reliability, observability, safety, and compliance in a regulated environment. Requires 10+ years of software engineering experience, including 2+ years building AI Agents for millions of users, with strong expertise in system architecture, platform design, and cross-org technical strategy.

Senior Data Scientist at SoFi focused on Anti-Money Laundering (AML) compliance. The role involves developing, optimizing, and validating AML models using machine learning and statistical methods, ensuring compliance with regulatory requirements, and contributing to AML data infrastructure and governance. This includes working with customer screening, transaction monitoring, and risk rating models across various product lines.

Senior Manager, Data Science for Portfolio Management and Loss Mitigation at SoFi. This role leads the development, deployment, and governance of portfolio management and loss mitigation models for credit products. The candidate will transition the team to next-generation machine learning platforms, leverage emerging data sources, and ensure adherence to Model Risk Management (MRM) standards in a regulated financial environment. Requires expertise in advanced ML techniques, Python, SQL, and regulatory knowledge (SR 11-7).

This role leads the Modeling & AI Enablement function within Marketing Data Science, focusing on building AI-driven competitive advantage and accelerating enterprise AI adoption. It involves defining strategy, developing AI-powered tools and platforms, guiding model development, ensuring responsible AI practices, and leading a team of data scientists. The role partners with Engineering, Marketing Technology, Risk, and Governance stakeholders.

Senior Staff Software Engineer to join Builder Tools engineering to lead the direction and architecture of AI-powered software testing experience, and elevate product reliability through testing infrastructure innovations and practices. This role involves technical leadership in AI powered agentic testing (autonomous test generation, execution, failure remediation), and foundational test infrastructure.

SoFi is seeking a Staff Data Scientist to lead the evolution of their Home Loan and Home Equity risk frameworks. This role involves designing and implementing ML models for credit underwriting, debt-to-income validation, and automated appraisal reviews. The candidate will provide technical oversight for externally developed models, present insights to leadership, collaborate with governance teams on regulatory compliance, and partner with Product and Engineering for model operationalization, deployment, and monitoring. The role also requires continuous exploration of ML frameworks to build proprietary mortgage risk tools.

Staff Marketing Data Scientist at SoFi, focused on building and scaling machine learning models for marketing and growth across financial products. The role involves developing predictive models for acquisition, conversion, retention, and LTV using behavioral, transactional, and credit data, with a strong emphasis on production implementation, monitoring, and regulatory compliance. The role also involves building feature stores and experimentation frameworks.

AI/ML Engineering Manager for Stripe's Payment Intelligence Suite, focusing on leading ML/AI adoption, deploying Foundation Models, and enhancing performance analytics and risk management. The role involves managing teams of managers and driving strategic execution for AI/ML across payments.

Machine Learning Engineer focused on developing and deploying deep learning models and foundation models for Stripe's payment products, impacting core business metrics. The role involves the entire ML lifecycle, from research to production, with an emphasis on reusable architectures and scalable workflows. Requires significant industry experience in end-to-end ML development and production deployment.

Stripe is seeking a Senior Machine Learning Engineer to own the end-to-end ML and agent architecture for the Stripe Assistant. This role involves developing an intelligent assistant that leverages LLMs and agentic systems to answer queries, resolve issues, provide business insights, and anticipate user needs. Responsibilities include setting strategy for high-trust actions, delivering accurate answers, orchestrating tools and agents, grounding responses in data, driving conversation continuity, establishing evaluation and SLOs, and improving quality, latency, cost, and availability. The role also involves mentoring engineers and upholding high standards for code quality, security, and operational rigor.

Stripe is seeking a Machine Learning Engineer for their Supportability Evaluation team. This role focuses on designing, building, training, evaluating, and deploying AI/ML models and large-scale systems for detection and decisioning within Stripe's financial ecosystem. The engineer will work on scaling an LLM-based system, integrating new capabilities through agentic approaches or supervised learning, and ensuring merchant compliance in real-time.

Machine Learning Engineer at Stripe focused on enhancing support experiences using AI. The role involves designing, building, training, evaluating, and deploying ML models, particularly LLMs, for applications like conversational agents, personalized documentation, and automated problem-solving. The engineer will work on RAG, tool use, agentic architectures, and post-training methods, collaborating with cross-functional teams to integrate AI into support systems and products.

Stripe is seeking PhD Machine Learning Engineering Interns to develop and deploy large-scale ML systems for financial infrastructure. Interns will work on end-to-end ML model development, including training and production deployment, focusing on areas like foundation models for fraud detection and user behavior prediction, and projects like the Stripe Assistant and Stripe Foundation Model.

Stripe's ML Infra team is seeking a Software Engineer to build and scale the ML lifecycle services, including training, serving, and LLM applications, to accelerate AI/ML adoption across the company. The role focuses on designing and implementing robust, high-availability infrastructure for production ML platforms.

Robinhood is seeking a Senior Machine Learning Engineer for their Agentic team to build and ship production AI agents for financial products. The role involves developing evaluation harnesses, feedback pipelines, implementing optimization techniques (DPO, PPO, reward modeling), launching fine-tuned models in production, and collaborating with research teams on agentic reasoning, planning, and tool use.

Robinhood is seeking a Senior Data Scientist to join their Fraud Data Science team. This role will focus on designing and deploying ML models for real-time fraud detection and prevention, analyzing behavioral data, developing data pipelines, and collaborating with engineering and product teams to enhance platform safety and integrity. The role requires experience in fraud detection or risk mitigation, proficiency in Python and SQL, and experience with ML frameworks.

Senior Data Scientist, ML role at Robinhood focused on building and improving recommendation system algorithms for personalization in prediction markets and other product surfaces. The role involves developing features and models, collaborating with engineers on pipelines and ranking systems, and designing experiments to evaluate performance. Requires experience in recommendation systems, Python, SQL, ML systems, and production modeling.

Senior Data Scientist, ML role focused on building, deploying, and iterating on predictive and causal ML models for incentive targeting and allocation in a fintech growth context. The role involves designing experiments, optimizing algorithms under constraints, and influencing long-term growth modeling strategy, including personalization and cross-sell optimization.

Robinhood is seeking a Senior Full Stack Software Engineer for their Agentic Apps team, focusing on Corporate Engineering and Business Systems. The role involves designing and building AI-powered internal applications and agents using LLMs and agentic systems. The engineer will work across frontend, backend, and AI orchestration layers to deliver user-friendly tools that improve internal workflows and contribute to broader AI initiatives. This is a product-driven role, not focused on research or model development, aiming to make agentic AI practical and production-ready.