Jobs at Together AI

Own the product strategy and developer experience for Together AI’s inference portfolio. Define product positioning across tenancy, capacity, performance, modalities, and workloads; improve APIs, SDKs, integrations, and customer workflows; translate infrastructure capabilities into usable products; shape pricing and packaging; and drive launches with engineering, research, sales, finance, and customers.

Owns full-cycle recruiting for Finance, Legal, and Business Strategy teams. Partners with executives and hiring leaders to develop hiring strategies, candidate profiles, and interview processes. Manages candidate experience through offer close, provides market intelligence and funnel analytics, and collaborates with recruiting partners to streamline hiring. The role requires strategic judgment, adaptability, relationship-building, and experience scaling teams in fast-paced technology environments.

Own production reliability for a Kubernetes-native GPU inference platform. Responsibilities include operating provisioning and host-lifecycle state machines, troubleshooting GPU, CUDA, InfiniBand, and RDMA failures, extending automated remediation, managing SLOs and error budgets, leading incident response, improving observability and inference efficiency, and partnering with ML platform and product teams to build reliable self-healing infrastructure.

Write, test, and publish developer documentation for inference, fine-tuning, and GPU products. Collaborate with engineers, product managers, and researchers; use AI tools and docs-as-code workflows; test documented features; file bugs; contribute to release notes; and use analytics and developer feedback to improve content.

Leads Together’s debt capital markets strategy and infrastructure financing initiatives. Responsibilities include managing lender and institutional capital relationships, executing complex financing transactions, negotiating credit agreements, structuring deals, preparing diligence materials, and partnering with legal, operations, engineering, product, and leadership teams. The role also supports strategic finance priorities and requires strong financial modeling, quantitative analysis, transaction execution, and executive communication skills.

Lead customer-facing post-training initiatives for large language models, including data strategy, algorithm selection, experiment design, evaluation, model selection, and RL workload sizing. Guide strategic customers from scoping through production, deliver measurable model-quality improvements, contribute code and reference implementations, and provide feedback to product and research teams. Requires deep hands-on expertise with LLM fine-tuning, reinforcement learning, experimentation, evaluation, datasets, open-source models, and production Python engineering.

Supports growth marketing through experimentation, campaign execution, and performance analysis across email, landing pages, paid media, social, and webinars. Develops hypotheses, launches tests, creates campaign content, audits tracking, and reports on signups, usage, retention, upsell, and revenue. Leads a capstone project and presents recommendations to marketing leadership while using data and AI tools to improve acquisition and conversion.

Senior Counsel will advise Product, Engineering, Research, Security, Infrastructure, and Sales on AI product development, privacy, licensing, patents, export controls, and sanctions compliance. Responsibilities include drafting product terms, leading privacy-by-design reviews, managing DPAs and data transfers, building export-control programs, advising on EAR and OFAC requirements, supporting patent strategy, and creating legal playbooks and training. The role requires pragmatic, business-oriented counsel in a fast-moving AI infrastructure company.

Own production reliability for a Kubernetes-native control plane provisioning and operating GPU inference fleets. Responsibilities include managing host lifecycle state machines, troubleshooting GPU, CUDA, InfiniBand, RDMA, and model-serving failures, extending automated remediation, owning SLOs and error budgets, leading incident response, improving inference performance and efficiency, and partnering across infrastructure, ML platform, research, and product teams.

Research intern developing speculative decoding algorithms and draft model architectures for faster large-language-model generation. Responsibilities include training, fine-tuning, and distilling models on distributed GPU clusters; designing rigorous experiments; optimizing serving implementations; evaluating realistic workloads; communicating results; and contributing research to production systems through technical writing and collaboration.

Source, engage, and nurture passive candidates for business-side roles across go-to-market, infrastructure strategy, and G&A. Partner with recruiters and hiring managers to define candidate profiles, craft personalized outreach, build talent pipelines, track sourcing metrics, and develop scalable sourcing systems. The role requires managing senior, hard-to-fill searches in a startup or hyper-growth environment while adapting across functions and using recruiting tools such as GEM, LinkedIn Recruiter, Noon AI, and Greenhouse.

Sources and evaluates elite engineering candidates through proactive outreach, GitHub review, and technically deep screening calls. Builds talent pipelines, runs outreach campaigns, assesses candidates’ technical depth and ownership, and partners with hiring managers and recruiters to calibrate hiring standards. The role requires an engineering background, strong technical fluency, persistence, and compelling written communication. Recruiting experience is optional and can be learned on the job.

Own investor relationships, equity fundraising, investor communications, and the company’s investment narrative. Lead board reporting, long-range planning, valuation and fundraise models, KPI development, investor targeting, diligence, and executive preparation. Translate AI infrastructure economics, financial performance, market trends, and investor sentiment into clear strategic insights for leadership, board members, and investors.

Leads Together AI’s partner marketing function by defining strategy, messaging, and co-marketing execution with NVIDIA and cloud partners. Collaborates with GTM, product marketing, demand generation, and field marketing teams to deliver campaigns, establish partner operating rhythms, track performance metrics, and improve ROI. The role requires building the function from the ground up and scaling partner go-to-market programs for a B2B AI or software platform business.

Lead platform infrastructure strategy and build reusable service infrastructure across Kubernetes, AWS, networking, and operational systems. Develop Helm charts, Terraform modules, CI/CD, GitOps workflows, zero-trust policies, and self-service platform capabilities. Partner with service, SRE, security, networking, and infrastructure teams to modernize services and drive adoption of shared patterns. Lead cross-company infrastructure initiatives and establish an initial platform infrastructure team.

Coordinates recruiting activities from interview scheduling through offers, background checks, onboarding transitions, and candidate communication. Maintains ATS records, prepares recruiting metrics, supports hiring managers and interviewers, improves recruiting processes, and ensures a positive candidate experience. Requires strong organization, communication, adaptability, confidentiality, and familiarity with recruiting tools and office productivity software.

Own full-cycle recruiting for go-to-market and business teams in a high-growth AI company. Partner with executives to define hiring strategies, candidate profiles, and interview processes; manage candidates through offer close; provide market intelligence and funnel metrics; and improve recruiting workflows and candidate experience. The role requires strategic leadership partnership, adaptability in ambiguity, and experience building recruiting processes from the ground up.

Lead architecture and implementation of Together AI’s global GPU cloud infrastructure. Own GPU and network virtualization, in-data-center IaaS, distributed scheduling and management planes, fault-tolerant automation, and customer-facing production systems. Set technical direction across teams, mentor engineers, establish engineering standards, and build scalable services spanning Kubernetes, compute, storage, networking, and GPU infrastructure.

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.

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.