Handshake

Senior Forward Deployed Engineer to embed within enterprise customer environments, define AI-driven solutions, build and deploy production-grade AI agents, and design/run evals to measure performance. The role requires full-stack ownership, deep understanding of customer business, and iteration until performance improves. Emphasis on real-world AI application shipping and systematic improvement of AI performance.

Handshake AI is seeking a PhD Student Researcher to work on novel RLHF/GRPO pipelines, instruction-following refinements, reasoning-trace supervision, multilingual/long-horizon/domain-specific benchmarks, automatic vs. human preference studies, robustness diagnostics, active-learning loops, data value estimation, synthetic data generation, and low-resource fine-tuning strategies. The goal is to produce an archive-ready manuscript or top-tier conference submission.

Software Engineer focused on building the core agent orchestration layer, including tool use, memory, and multi-step reasoning systems, to power AI-driven features for millions of users. The role also involves designing evaluation, observability, and reliability frameworks for agent behavior and establishing engineering standards for agentic development.

Senior Software Engineer to architect and build the foundational systems for AI agents, including tool use, memory, and multi-step reasoning. The role involves designing evaluation and observability frameworks, establishing engineering standards for agentic development, and partnering with ML/product teams to ship agent-powered features.

Technical Lead Manager for Handshake AI, focusing on building and shipping production AI solutions. This player-coach role requires hands-on coding, system architecture, and team leadership, working with frontier AI labs on data, evals, and AI systems.

Staff Software Engineer to lead the architecture and evolution of Handshake's Reinforcement Learning Environments (RLE) platform, focusing on scalable systems, data pipelines, and enabling rapid domain creation for frontier AI models. This role involves technical leadership, system design, and cross-team collaboration to ensure reliability, observability, and performance.

Senior Engineering Manager to lead the Reinforcement Learning Environments (RLE) team, responsible for building interactive sandboxes that simulate end-to-end workflows for frontier models. The team generates high-signal interaction data used for training and evaluating models on task completion, quality, and robustness. The role involves leading a team of engineers, owning the RLE roadmap, driving architecture for scalable systems, and ensuring reliability and data quality.

This role involves leading coding data initiatives for AI and platform teams, coordinating SWE Fellows, designing and owning technical evaluation and annotation workflows, and ensuring delivery, margins, quality, and customer relationships. Responsibilities include writing and validating coding assessments, building rubric-driven code review processes, instrumenting quality signals, and adapting workflows. The role requires strong technical and analytical skills, coding proficiency, and stakeholder management.

This role involves designing and evaluating chemistry prompts for AI models, focusing on scientific reasoning and identifying model breakdowns. The specialist will act as a subject matter expert in electrochemistry and functional materials, applying adversarial prompting and assessing the accuracy of AI-generated responses.

This role focuses on evaluating AI models in chemistry, specifically using expertise in organic chemistry, polymer chemistry, and spectroscopy (NMR) to design prompts, assess model outputs, and identify reasoning errors. The specialist will contribute to quality standards and provide feedback to the AI team.

Role focuses on evaluating AI models in chemistry, designing prompts, assessing outputs, and identifying reasoning errors. Requires PhD in Chemistry and experience in AI data annotation or RLHF.

The role focuses on evaluating and stress-testing complex scientific prompts for large language models, specifically in biology. The specialist will design high-difficulty prompts, identify reasoning errors and weaknesses in model outputs, and apply adversarial prompting techniques. This is a research-oriented role focused on improving AI model capabilities through expert evaluation.

This role focuses on evaluating AI models, specifically in physics, by crafting and assessing challenging problems, probing model reasoning, and identifying failures using adversarial prompting. It involves providing expert critique of AI responses and ensuring quality benchmarks are met. Prior experience in AI data annotation or RLHF is required, with a PhD in Physics or a related field.

Technical Lead Manager, Forward Deployed Engineering at Handshake AI. This player-coach role involves shipping end-to-end AI solutions for strategic partners, designing and building integrations, tooling, APIs, and workflows, and managing a small team of FDEs. The focus is on building production-ready systems and scaling team output through reusable components, while staying hands-on with coding.

Machine Learning Engineer Intern at Handshake AI, focusing on building intelligent product experiences for job seekers. The role involves developing, evaluating, and deploying ML models for search, recommendations, and matching systems in a production environment. Requires a PhD candidate with Python, PyTorch/TensorFlow, and ML operations experience.

Software Engineer to build and scale Reinforcement Learning Environments (RLE) platform, which are interactive systems for frontier AI models to learn real-world tasks. The role involves owning components end-to-end, designing backend systems and data pipelines, and improving system reliability and performance, supporting model training and evaluation.

Software Engineer on the Coding Pod will build data infrastructure and pipelines for frontier AI coding models, focusing on creating large-scale, high-quality benchmark datasets for evaluating model performance on coding tasks. This role involves owning end-to-end data pipelines, integrating with developer ecosystems, and working with evaluation systems and agentic coding tools.

Associate Software Engineer to build Reinforcement Learning Environments (RLE) platform, including supporting infrastructure, backend systems, frontend interfaces, and data pipelines for model training and evaluation. The role involves creating modular workflow domains and working with senior engineers to improve system reliability and performance.

Software Engineer to build and scale the Reinforcement Learning Environments (RLE) platform, which involves designing and implementing backend systems, data pipelines, and modular workflow domains to support frontier AI model training and evaluation. The role requires experience in backend/distributed systems, ML-adjacent infrastructure, and cloud technologies.

Senior Software Engineer to build and scale Reinforcement Learning Environments (RLE) platform, simulating real-world workflows for AI model training and evaluation. This role involves driving architecture for scalable systems and data generation pipelines, partnering with research and product teams, and ensuring system reliability and observability.

Senior Forward Deployed Engineer to serve as a technical leader at the intersection of engineering and strategic customers (leading AI labs). Owns end-to-end lifecycle of high-impact deployments, architecting, building, and scaling solutions to improve customer workflows and model performance. Operates across the stack in ambiguous, fast-changing environments.

Machine Learning PhDs needed for hourly contract work to evaluate AI-generated content and provide feedback on machine learning reasoning, proof construction, and technical problem-solving. This role focuses on assessing AI responses for accuracy, rigor, and relevance to real-world physics research.

This Machine Learning Engineer role focuses on developing and deploying ML models that directly impact user experience and business metrics for a consumer platform. The role involves end-to-end ownership of the ML lifecycle, working with cutting-edge infrastructure like embedding-based retrieval and multi-stage rankers, and contributing to responsible AI practices.

Senior Engineering Manager to lead and scale a team of Forward Deployed Engineers (FDEs) focused on customer-facing AI solutions for strategic partners. The role involves building the organizational structure, managing technical execution, and ensuring reliability and maintainability of AI products.

Associate Machine Learning Engineer for the Growth Relevance team, focusing on developing, deploying, and enhancing ML systems for lifecycle optimization, personalized notifications, and monetization strategies. The role involves working with embedding-based retrieval, GNNs, and multi-stage rankers, and contributing to responsible AI practices.

Staff Forward Deployed Engineer role at Handshake AI, focusing on defining and driving technical strategy for engineered solutions to strategic customers, including leading AI labs. The role involves architecting and delivering production-grade systems, setting technical direction, and influencing product and platform architecture. It requires deep customer engagement and scaling forward-deployed engineering as a function, with a strong emphasis on customer-facing AI products.

Software Engineer role focused on building core consumer experiences, including agentic AI features for students using OpenAI APIs and agentic frameworks. The company also has a separate AI data business focused on frontier AI labs.

Manager, Strategic Projects leading a team focused on AI data and evaluation work. Responsibilities include managing SPLs, driving project delivery (data pipelines, labeling workflows), translating needs into plans, owning performance metrics, ensuring a good experience for fellows, and partnering with Product/Engineering on tooling. Success involves consistent delivery, improved operational metrics, and strong team leadership. Requires 5+ years in operations, 2+ years managing teams, and experience with complex projects, ideally in AI data operations or ML ops.