Jobs at Together AI

Conduct research on frontier AI agents, including alignment, reliability, long-horizon reasoning, dataset design, evaluation, safety, and scalable infrastructure. Develop novel techniques, run rigorous experiments, document findings in publications and blog posts, and communicate project progress. Work may involve machine learning, deep learning, reinforcement learning, spoken-input agents, and foundation model development.

Software Engineer Interns will design, develop, and maintain software across platform, infrastructure, inference, and product engineering teams. Responsibilities include building features, writing maintainable code, collaborating cross-functionally, participating in code reviews, debugging, and optimizing performance. The 12-week, on-site internship is based at the company headquarters in San Francisco and may involve cloud infrastructure and open-source projects.

Develop and optimize GPU-accelerated kernels and algorithms for AI and machine learning applications. Collaborate with modeling, algorithm, hardware, and software teams on GPU architecture and programming-model co-design. Integrate GPU solutions into software systems, optimize performance and scalability, and track advances in GPU programming techniques. The internship lasts 12–14 weeks and includes cross-functional research and engineering work.

Conduct frontier AI research focused on agentic systems, alignment, reliability, evaluation, datasets, self-learning, and long-horizon reasoning. Responsibilities include implementing novel techniques, designing rigorous experiments, documenting findings in publications and blog posts, and communicating project results. Work may involve training and evaluating models, developing safety paradigms, building spoken-input agents, and creating scalable ML infrastructure.

Develop automated tests for website and service functionality, performance, and reliability. Improve test automation frameworks, expand coverage, write maintainable code, and collaborate with engineering and product teams on test plans and quality standards. The intern will gain exposure to AI infrastructure, model benchmarking, API testing, and CI automation.

Develop and optimize GPU-accelerated kernels and algorithms for ML/AI applications. Collaborate with modeling, algorithm, hardware, and software teams on GPU and programming-model co-design, integrate optimized solutions, and apply performance profiling techniques to improve scalability and efficiency.

Software Engineer Interns will design, develop, and maintain software across platform engineering, infrastructure, SRE, and inference teams. Responsibilities include building product features, writing maintainable code, collaborating cross-functionally, participating in code reviews, debugging, and optimizing performance. Interns will contribute to projects from initial scope through production and may work on cloud infrastructure or open source initiatives.

Conduct research on efficient, scalable inference systems for foundation models. Design experiments, develop cross-layer optimizations across models, software, and hardware, and investigate distributed inference, speculative decoding, KV cache design, compiler optimization, and large-scale serving. Communicate project progress and results, and document findings in scientific publications and blog posts.

Conduct research on efficient, scalable inference systems for foundation models. Design experiments, develop cross-layer optimizations across models, software, and hardware, and investigate distributed inference, speculative decoding, KV cache design, and serving architectures. Communicate project results and document findings through scientific publications and blog posts.

Early-career software engineer designing, developing, and maintaining software across systems, infrastructure, machine learning, inference, and user-facing products. Responsibilities include collaborating cross-functionally, owning projects, participating in code reviews, debugging, optimizing performance, and contributing to technical direction. Candidates should have strong computer science fundamentals, hands-on development experience, Git proficiency, creative problem-solving ability, and effective communication skills.

Lead Together AI’s technical community engagement strategy across X, LinkedIn, and Discord. Own content strategy, grow developer and researcher engagement, collaborate with marketing, developer relations, and solutions teams, and experiment with new content formats, competitions, and community initiatives. The role is primarily online, with some responsibility for in-person meetups and events. It requires developer marketing and community engagement experience, strong knowledge of AI developers, social media fluency, ownership, and data-driven experimentation.

Own and build Together AI’s brand marketing function by developing the brand narrative, campaigns, messaging, visual identity, channel execution, and measurement strategy. Lead internal and vendor design resources, manage brand budgets, guide thought leadership, track awareness and sentiment, and foster experimentation to scale high-performing brand initiatives.

Build and maintain GTM data infrastructure, dashboards, reports, and reliable data products. Analyze data from Salesforce, Amplitude, production systems, and billing using SQL. Develop foundational knowledge for contributing to dbt models in Snowflake, partner with cross-functional stakeholders, and communicate findings to non-technical audiences.

Own strategic AI-native and enterprise accounts across the full sales cycle, from pipeline generation and prospecting through closing and expansion. Partner with Solutions, Engineering, and Research teams to design technical solutions and proof-of-concept evaluations for inference and post-training workloads. Build executive and technical relationships, manage complex sales cycles, identify customer use cases, provide product feedback, and improve sales processes. The role requires strong B2B technology sales experience, technical fluency, and a record of closing complex strategic deals.

Operate and scale multi-petabyte distributed storage for AI and HPC workloads. Design high-performance parallel filesystems, object stores, caching architectures, and Kubernetes-native storage operators. Optimize data paths, throughput, multi-tenancy, cost, and reliability across thousands of GPU nodes. Integrate technologies such as Vast, Weka, Ceph, and Lustre; develop automation and tooling in Go and Python; and contribute to storage infrastructure, observability, benchmarking, and open-source projects.

Provide technical support for AI platform customers, troubleshooting APIs, model workflows, deployments, authentication, billing, API keys, and organization access. Triage tickets, reproduce issues, communicate incidents and maintenance updates, configure enterprise SSO, investigate billing problems, and coordinate escalations with Engineering, Research, Product, and Finance. The role requires strong customer communication, API troubleshooting, SaaS billing knowledge, SAML/OIDC familiarity, infrastructure awareness, and experience with support platforms.

Own account-based marketing programs and integrated demand-generation campaigns targeting AI-native and digital-native companies. Partner with Sales, Field Marketing, RevOps, Product Marketing, Brand, Design, and Content to build campaigns across paid media, email, events, content, webinars, and account-based experiences. Lead experimentation, campaign measurement, marketing-stack decisions, account penetration, and pipeline contribution against new-logo goals.

Develop and optimize large-language-model inference and post-training systems for strategic customers. Configure inference engines, tune performance, and implement techniques including KV caching, speculative decoding, parallelism, and quantization. Lead fine-tuning and reinforcement-learning workflows, guide production deployments, support customer onboarding, and provide product feedback. Partner with Solutions Architecture, Engineering, Sales, and customers to deliver successful proofs of concept, platform adoption, and tailored model optimization.

Surface, qualify, and convert inbound and outbound opportunities for GPU and AI API platform sales. Partner with Account Executives to develop commercial contracts, expand platform users, manage CRM pipelines, generate interest across multiple channels, and relay customer feedback to product teams. The role requires high-volume prospecting, strong communication, technical curiosity, adaptability, and a customer-focused approach to selling AI infrastructure products.

Build, profile, and optimize Together AI's large-scale training infrastructure. Integrate and validate new model architectures, eliminate performance bottlenecks across compute/memory/communication, and productionize research methods. Enable reliable, scalable fine-tuning for customers and maintain experimental infrastructure to accelerate research-to-production workflows.