Together AI
Data AI · Open-source model infra
- HQ
- San Francisco, US
- Website
- together.ai
Data AI · Open-source model infra
Together AI currently has 25 active AI-related job listings. The majority of these roles, 80%, are focused on serving infrastructure. Engineering is the dominant function with 21 listings, and the United States is the primary hiring country with 19 roles. Frequent tech tags include model serving, inference infrastructure, and fine-tuning. Over the last 30 days, Together AI posted 4 new AI roles, representing a 33% decrease compared to the previous 30-day period.
Currently tracking 22 active AI roles, down 17% versus the prior 4 weeks. Primary focus: Serve · Engineering. Salary range $121k–$300k (avg $227k).
Together AI currently has 26 active AI-related roles in our index. The most common open titles are: Solutions Architect (2), AI Infrastructure Engineer, AI Researcher, Core ML (Turbo), AI infrastructure Engineer (SRE) Bangalore , Customer Support Engineer (Inference), India. Most positions are in Engineering and Research.
Together AI's active AI hiring is concentrated in: serving infrastructure (81%), post-training (12%), application (4%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Together AI is hiring AI talent in: United States (20 roles), Netherlands (3 roles), United Kingdom (1 role).
Job postings at Together AI most frequently mention: Production ML Systems, Inference Infrastructure, GPU Computing, LLM Inference, Text-to-Speech.
In the past 30 days, Together AI has posted 5 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Research Engineer, Core ML Research Engineer role focused on improving inference efficiency and unifying it with RL/post-training systems for production-grade AI APIs. The role involves end-to-end ownership of critical systems, translating frontier ideas into robust infrastructure, and shipping measurable improvements in latency, throughput, cost, and model quality at scale. | ServePost-train | 10 |
| Forward Deployed Engineer (Inference & Post-Training) - Mandarin Speaking Forward Deployed Engineer (FDE) focused on Inference & Post-Training for strategic customers. Responsibilities include optimizing inference engines, tuning performance for POCs and deployments, and guiding customers through fine-tuning pipelines (LoRA, SFT, DPO, RLHF, GRPO). Acts as a technical point of contact for strategic accounts, ensuring successful platform adoption and influencing product roadmap. |
| ServePost-train |
| 9 |
| Research Engineer, Large-Scale Training Research Engineer focused on scaling and optimizing large-scale training infrastructure for foundation models, integrating new architectures, and productionizing novel training methods. | Post-trainData | 9 |
| Research Engineer, Post-Training Inference Research Engineer focused on customizing open-source foundation models for downstream applications. The role involves building and improving services for fine-tuning, reinforcement learning, and evaluation, with a strong emphasis on integrating post-training processes with production serving and optimizing inference for RL training workloads. Requires experience in building and deploying ML services, modern inference engines, and fine-tuning techniques. | Post-trainServe | 9 |
| Research Intern RL & Post-Training Systems, Turbo (Fall 2026) Research intern focused on making post-training and reinforcement learning for large language models efficient, scalable, and reliable, by co-designing algorithms and systems at the intersection of RL, inference, and large-scale experimentation. | Post-trainServe | 9 |
| Research Intern, Model Shaping (Fall 2026) Research intern role focused on advanced post-training methods (supervised learning, preference optimization, RL), efficient neural network training techniques, and robust evaluation of foundation models. The team tailors open foundation models for downstream applications and develops new methods for efficient training and evaluation. | Post-trainPretrain | 9 |
| Systems Research Engineer Intern - GPU Programming (Fall 2026) Internship role focused on optimizing GPU-accelerated kernels and algorithms for ML/AI applications, co-designing GPU kernels and model architecture with modeling teams, and contributing to efficient GPU architectures and programming models. | Serve | 9 |
| Research Intern, Inference (Fall 2026) Research intern focused on building efficient, scalable, and reliable serving systems for large foundation models, involving distributed inference, compiler optimization, and hardware optimization. | Serve | 9 |
| Frontier Agents Intern (Fall 2026) Research intern focused on building, aligning, and scaling frontier AI agent systems for complex multi-step tasks across text and speech. Projects involve developing new training methods, curating datasets, studying failure modes, and building scalable infrastructure for agent operations. | AgentPost-train | 9 |
| Staff Machine Learning Engineer, Voice AI Staff ML Engineer focused on optimizing the model serving layer for voice AI applications, including speech-to-text and text-to-speech models, with a focus on latency, throughput, and GPU utilization using inference engines like TRT-LLM and SGLang. The role involves building evaluation frameworks, supporting model partners, and shaping the architecture for next-generation voice models. | Serve | 9 |
| Forward Deployed Engineer (Inference & Post-Training) Forward Deployed Engineer focused on optimizing inference engines and fine-tuning pipelines for production AI teams, acting as a technical partner to strategic customers. Responsibilities include inference engine optimization, performance tuning, post-training/fine-tuning (LoRA, SFT, DPO, RLHF, GRPO), customer alignment, onboarding, and providing product feedback. | ServePost-train | 9 |
| Senior Machine Learning Engineer, Voice AI Senior ML Engineer focused on optimizing the model serving layer for voice AI workloads, including speech-to-text and text-to-speech models. The role involves hands-on work with inference engines, GPU optimization, batching strategies, and ensuring new model architectures can be productionized efficiently. The goal is to achieve best-in-class latency and reliability for real-time voice applications. | Serve | 9 |
| Research Engineer, Frontier Speculative Decoding Research Engineer focused on translating internal model training research into production-ready deployments by fine-tuning general-purpose models into specialized tools. This involves designing novel speculative algorithms, data curation, hyperparameter tuning, and checkpoint evaluation, with a focus on accuracy-efficiency tradeoffs for generative AI models. | Post-trainServe | 9 |
| Systems Research Engineer, GPU Programming This role focuses on optimizing and developing GPU-accelerated kernels and algorithms for ML/AI applications, requiring expertise in GPU programming (CUDA, Triton) and performance profiling. The engineer will collaborate with modeling, hardware, and software teams to enhance AI system efficiency and co-design GPU architectures. | Serve | 9 |
| AI Researcher, Core ML (Turbo) AI Researcher focused on the intersection of efficient inference algorithms, architectures, engines, and post-training/RL systems for production-scale API services. The role involves advancing inference efficiency, unifying inference with RL/post-training, and owning critical systems. | ServePost-train | 9 |
| Technical Support Engineer (Inference) - US Weekends Technical Support Engineer role focused on supporting customers using Together AI's inference and fine-tuning services, acting as a customer-facing SRE for inference endpoints on Kubernetes. Responsibilities include resolving technical challenges, ensuring endpoint health, managing incidents, contributing infrastructure changes for model deployment, and collaborating with engineering and product teams. Requires experience with Kubernetes, AI/ML/GPU technologies, infrastructure services, observability tooling, and LLM inference frameworks. | Serve | 8 |
| Staff Engineer, Distributed Storage and HPC & AI Infrastructure Staff Engineer focused on designing and delivering multi-petabyte storage systems optimized for AI training and inference workloads. Responsibilities include architecting high-performance parallel filesystems and object stores, building Kubernetes-native storage operators, optimizing data paths for high throughput, and implementing intelligent caching and data distribution strategies. The role requires deep expertise in distributed storage systems, Kubernetes, and programming in Go and Python. | Serve | 8 |
| Forward Deployed Engineer (GPU Clusters) The Forward Deployed Engineer (FDE) will be a technical partner to customers building large-scale AI models, focusing on GPU cluster infrastructure, networking, storage, and orchestration to ensure stability, optimize performance, and facilitate platform adoption. This role involves hardening clusters, tuning orchestration layers (Kubernetes/SLURM), debugging low-level bottlenecks, building reference designs, and leading benchmarking exercises. | Serve | 8 |
| Engineering Manager, Model Serving Engineering Manager for Together AI's Model Serving platform, focusing on delivering world-class inference and fine-tuning in public APIs and customer deployments. Responsibilities include owning SLAs, improving testing/deployment/monitoring, building self-serve tooling, defining configuration best practices for inference engines, leading incident response, and mentoring team members. Requires 5+ years operating production ML inference or training systems at scale and 2+ years in senior IC or tech lead roles, with deep expertise in Kubernetes, multi-cluster orchestration, and ML serving frameworks. | ServePost-train | 8 |
| Machine Learning Engineer Machine Learning Engineer at Together AI focused on developing and scaling production systems for LLM inference and fine-tuning APIs. Requires strong experience in high-performance, distributed systems and the LLM inference ecosystem. | ServePost-train | 8 |
| Machine Learning Engineer - Inference Machine Learning Engineer focused on optimizing and enhancing the performance of AI inference systems, working with state-of-the-art large language models to ensure efficient and effective operation at scale. Responsibilities include designing and building production systems, optimizing runtime inference services, and creating supporting tools and documentation. | Serve | 8 |
| Staff Platform Engineer, Voice AI Staff Platform Engineer for Together AI's Voice AI platform, focusing on the architecture and reliability of real-time API layers, autoscaling for latency-sensitive workloads, and building the observability platform for voice infrastructure. The role requires deep expertise in distributed systems, real-time streaming, and Kubernetes, with a strong product intuition for developer platforms. | Serve | 7 |
| AI Infrastructure Engineer AI Infrastructure Engineer responsible for keeping user-facing services and production systems running smoothly, specializing in systems, availability, reliability, and scalability, with interests in algorithms and distributed systems. Builds and runs infrastructure using Ansible, Terraform, and Kubernetes, and develops monitoring systems. | Serve | 7 |
| Senior Platform Engineer, Voice AI Senior Platform Engineer for Together AI's Voice AI platform, focusing on the API and infrastructure layer for real-time speech-to-text and text-to-speech models. The role involves building WebSocket and HTTP APIs, designing autoscaling for latency-sensitive streaming, and ensuring platform reliability for production voice agents. | Serve | 7 |
| Senior Backend Engineer, Inference Platform Senior Backend Engineer focused on building and optimizing the inference platform for advanced generative AI models, including LLMs and multimodal models, at scale. The role involves optimizing latency, throughput, and resource allocation across tens of thousands of GPUs, collaborating with researchers to productionize frontier models, and contributing to open-source inference projects. | Serve | 7 |
| Machine Learning, Platform Engineer Machine Learning Platform Engineer at Together AI, focusing on building a container platform, optimizing autoscaling, minimizing cold starts, and improving end-to-end model performance for custom models and dedicated inference. The role involves optimizing inference across the stack, including CUDA kernels, PyTorch, inference engines, and container orchestration. | Serve | 7 |
| Platform Engineer, Model Shaping Platform Engineer focused on building and operating the foundational infrastructure for Together AI's model customization and evaluation platform. This includes backend services, scaling production workflows, and a job orchestration platform across datacenters and heterogeneous hardware. The role emphasizes reliability, CI/CD, and cloud/hybrid environment management. | ServeData | 7 |
| AI Infrastructure Engineer AI Infrastructure Engineer responsible for keeping user-facing services and production systems running smoothly, applying engineering principles and automation to operating environments. Focuses on systems, availability, reliability, and scalability, with interests in algorithms and distributed systems. Builds and runs infrastructure using Ansible, Terraform, and Kubernetes, and designs monitoring systems. | Serve | 7 |
| Senior Software Engineer - Together Cloud Infrastructure Senior Software Engineer focused on building and operating a high-performance, global AI cloud infrastructure platform. This includes designing and maintaining backend services for hardware management, IaaS software layer for GPU data centers, high-performance object storage for pretraining datasets, and advanced observability stacks for distributed pretraining. The role also involves architecture and research for decentralized AI workloads and contributing to the open-source platform. | ServeData | 7 |
| Solutions Architect Solutions Architect at Together AI to work with customers and prospects to create business value through Generative AI applications. This role involves acting as a technical advisor, running demonstrations and POCs, collaborating with sales, building relationships with customer leadership, delivering feedback to product/engineering/research, and building educational content. Requires 5+ years in a customer-facing technical role with 2+ years in pre-sales, strong technical background in AI/ML/GPU, understanding of LLM training/fine-tuning/inference, Python/JavaScript proficiency, and familiarity with infrastructure services. | Serve | 7 |
| Technical Support Engineer (GPU Clusters) - US Weekends This role is a customer-facing Technical Support Engineer focused on supporting customers using Together AI's GPU clusters for training, fine-tuning, and inference. The engineer will act as a product expert and SRE, troubleshooting complex technical challenges related to Kubernetes, GPU hardware, networking, and storage. They will collaborate with engineering and product teams to drive improvements and transform customer insights into product roadmap actions. The role requires experience with AI/ML infrastructure, Kubernetes, and high-performance computing environments. | — | 5 |
| Software Engineer, Customer Insights Software Engineer for Customer Insights team at Together AI, focusing on building and operating customer-facing visibility layers for AI workloads. This role involves developing features for analytics, activity history, audit logs, and investigation workflows, partnering with Data Platform and Observability teams. The position requires backend engineering skills and experience with large-scale distributed systems, with an emphasis on learning event-driven systems and analytics pipelines. | — | 5 |
| Senior Product Engineer, Fullstack Fullstack engineer to build and improve Together AI's core product surfaces (Playground, Model Garden, Fine-Tuning, etc.), focusing on user experience and shipping features end-to-end. Requires strong JavaScript/TypeScript, React, and full-stack fluency, with a pragmatic approach and curiosity about AI/LLMs. Experience with agentic workflows is a plus. | Ship | 5 |
| Technical Program Manager, Compute Qualification This role is responsible for qualifying new compute capacity for Together AI, ensuring it meets technical standards for training and inference workloads. The Technical Program Manager will manage the end-to-end qualification process, coordinating with engineering partners, reviewing specifications, and making go/no-go recommendations. The role requires strong technical understanding of data center infrastructure and program management skills. | — | 5 |
| Staff Software Engineer, GPU Infrastructure Lifecycle Management Software Engineer to build systems that treat infrastructure as software, owning state machines that provision hardware, bring it into service, and manage its full lifecycle for running inference clusters. The role focuses on creating a self-service API for the inference team to manage clusters, automating self-healing, and ensuring the reliability of the provisioning pipeline. It requires strong software engineering background, experience with workflow orchestration tools, and building control planes or event-driven systems. | — | 5 |
| GTM Engineer This role focuses on building and architecting an AI-native tech stack for Go-To-Market (GTM) operations, including CRM integrations, data centralization, and automation of sales processes using AI agents. The primary goal is to drive revenue outcomes by translating GTM strategy into scalable systems. While the company is an AI research firm, this specific role is not directly involved in AI/ML model development but rather in leveraging AI tools and building systems that support AI-driven business operations. | — | 5 |
| Product Manager, AI Infrastructure Product Manager for AI Infrastructure at Together AI, focusing on GPU clusters, managed storage, and observability. The role involves owning day-to-day product work, identifying and resolving issues, running experiments, and translating customer needs into product decisions. The goal is to grow into full ownership of a product area within nine months. Requires strong technical foundation, data/analytics skills, and customer empathy for AI-native startups. | — | 5 |
| Manager, Infrastructure Strategy & Operations This role focuses on the strategy, operations, and analytical backbone for scaling compute infrastructure at an AI-native cloud company. It involves research, benchmarking, and decision frameworks for sourcing, evaluating, and deploying compute, with a focus on market intelligence, site comparisons, and operational analysis. Responsibilities include building dashboards for visibility into costs and utilization, developing comparison frameworks for sourcing decisions, and evaluating data center sites and energy options. The role requires strong quantitative skills, experience in high-growth startups or AI companies, and familiarity with AI productivity tools. | — | 5 |
| Customer Support Engineer (Inference) Customer Support Engineer role focused on supporting customers with Together AI's inference and fine-tuning services, GPU clusters, and Gen AI solutions. The role involves resolving complex technical challenges, acting as a product expert, collaborating with engineering and product teams, and transforming customer insights into product roadmap improvements. Requires strong technical background in AI, ML, GPU technologies, HPC environments, and familiarity with infrastructure services and Python. | ServePost-train | 5 |
| Senior Technical Recruiter, AI/ML Research Senior Technical Recruiter for Together AI, a company building an AI Native Cloud. The role focuses on scaling world-class AI research and engineering teams by partnering with leadership, leading full-cycle recruiting for specialized AI talent, and providing market intelligence. | — | 5 |
| Engineering Manager, Site Reliability Engineering Engineering Manager for Site Reliability Engineering (SRE) to lead a team of ~10 engineers responsible for Together AI's production infrastructure, including bare-metal GPU compute, public-cloud Kubernetes for inference, and Kubernetes with virtualization for virtual clusters. The role involves a mix of management (50-60%) and hands-on technical work (40-50%), focusing on shifting the team from reactive, manual operations to systemic, automation-first work, improving incident response, and developing engineers. | — | 5 |
| Junior Technical Program Manager — Infrastructure Operations This role focuses on the operational management of a large GPU fleet, ensuring nodes are online, GPUs are performing, and datacenter transitions are smooth. It involves owning the end-to-end node lifecycle, driving remediation, managing project timelines for new datacenter bring-ups, diagnosing utilization loss, and building dashboards for visibility and accountability. The environment is fast-paced and requires figuring things out alongside engineers building at the frontier. | — | 5 |
| Staff Engineer, Customer Insights Staff Engineer to build and scale the customer-facing visibility layer for Together's AI Cloud, focusing on historical analytics, activity history, audit logs, event timelines, notifications, and investigation workflows. The role will evolve these foundations into AI-first investigation and insight workflows that summarize activity, explain anomalies, and provide trustworthy context for human operators and autonomous agents. This is a hands-on role designing event, query, delivery, and governance systems, and building user-facing workflows for enterprise customers. | — | 5 |
| Technical Account Manager (TAM), AI Factory This role is a Technical Account Manager focused on the infrastructure supporting large-scale AI GPU deployments for a strategic enterprise customer. The TAM will be the primary technical point of contact, responsible for the end-to-end technical relationship across compute, networking, storage, and facilities, ensuring smooth delivery and operational health. Responsibilities include issue lifecycle management, hardware lifecycle management, advising on infrastructure stack best practices, owning the observability strategy, coordinating operations, and managing capacity expansions. The role requires deep expertise in GPU infrastructure, large-scale networking, enterprise storage, and DC operations, with experience in customer-facing technical roles and AI/HPC infrastructure. | — | 5 |
| Director, Support Engineering This role leads and scales the customer support function for Together AI, focusing on both API support (serverless/dedicated inference, billing) and GPU support (large-scale training infrastructure). It's a player-coach position requiring hands-on involvement in complex escalations, managing support engineers, defining KPIs, and improving support workflows and tooling. The role requires strong technical depth in AI infrastructure, distributed systems, and experience with SLA-driven operations. | — | 5 |
| Customer Support Engineer (GPU Cluster) Customer Support Engineer role focused on supporting customers using Together AI's GPU clusters for training, fine-tuning, and inference. The role involves resolving complex technical challenges, acting as a product expert, and collaborating with Engineering and Product teams. Requires experience in customer-facing technical roles, familiarity with AI/ML, GPU technologies, and infrastructure services like Kubernetes. | — | 5 |
| Sr. Partnerships Manager, Model Ecosystem This role is responsible for building and managing the model ecosystem for Together AI, focusing on negotiating deals with model builders to bring proprietary and open-source models onto the platform. It involves working closely with Product, Finance, and Marketing to ensure the model roadmap is technically superior, commercially viable, and market-facing. The role requires strong deal-making, technical curiosity, and experience in business development or strategic partnerships within developer platforms. | — | 5 |
| Backend Software Engineer — Data Platform & AI Data Products Backend Software Engineer focused on building data platform infrastructure and LLM-adjacent data products. The role involves designing and developing backend services for event streams, access layers, and APIs, as well as creating services for prompt categorization, enrichment, and metadata. The engineer will apply AI augmentation mindset to their own development and the systems they build, with a focus on production backend systems, distributed systems, and data modeling. | Serve | 5 |
| Lead Product Designer Lead Product Designer to craft user experiences for technical AI development tools, shape AI development, and establish design standards for a growing organization. This role involves leading UX initiatives, elevating design quality, and collaborating with Engineering, Product, and Marketing. | — | 5 |
| Senior Software Engineer, Observability Senior Software Engineer focused on building and scaling a robust observability platform for AI infrastructure, including metrics, logs, traces, monitoring, alerting, and anomaly detection. The role involves designing and implementing scalable systems using tools like Prometheus, Grafana, ClickHouse, OpenTelemetry, Go, Python, and Terraform, with a focus on distributed systems, containerization, and orchestration. | — | 5 |