Jobs at Together AI

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.

Operate and scale Together AI's production infrastructure: run GPU-enabled Kubernetes clusters, automate with Ansible and Terraform, build monitoring/observability, respond to incidents (on-call), debug production issues, design deployment/upgrade processes, and plan infrastructure growth for high availability and scalability.

Build production software that provisions and manages GPU hosts and inference clusters through their full lifecycle. Design declarative APIs, control planes, state machines, reconciliation loops, durable workflows, event-driven automation, and self-healing systems. Partner with inference and ML platform teams to encode cluster requirements, while owning reliability, testing, deployment, and production operations.

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.

Provides customer-facing technical support for enterprise GPU clusters, acting as an SRE and product expert. Responsibilities include monitoring cluster health, troubleshooting Kubernetes, Slurm, storage, networking, and hardware issues, maintaining production infrastructure, and coordinating escalations with Engineering, Product, Research, and Sales. The role also documents procedures, identifies recurring support patterns, supports roadmap improvements, and provides weekend, holiday, and occasional on-call coverage.

Build production software for automated GPU and inference cluster provisioning. Design declarative APIs, control planes, state machines, reconciliation loops, durable workflows, and self-healing systems that manage hardware from discovery through deployment, operation, repair, and decommissioning. Partner with inference and ML platform teams to model cluster topology and scheduling requirements. Own reliability, testing, CI/CD, deployment, and production operations for the platform.

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.

Conduct research on speculative decoding for large language models, including developing drafter architectures, verification schemes, distillation methods, and adaptive training approaches. Train and fine-tune models on distributed GPU clusters, evaluate algorithms under realistic serving workloads, optimize implementations for inference engines, communicate findings, and contribute successful research to production.

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.

Architect and build Together AI’s globally distributed GPU cloud platform, including virtualization, Kubernetes-based IaaS, GPU scheduling, networking, storage, fault-tolerant control planes, monitoring, and automated remediation. Set technical direction, lead architecture and design reviews, drive cross-team alignment, mentor engineers, and establish engineering standards for reliable, high-performance AI infrastructure at massive scale.

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.

Design and implement security solutions for AI infrastructure across hardware, firmware, operating systems, and cloud services. Assess platform security, automate telemetry and configuration-drift detection, build AI-assisted security applications, support secure device lifecycle management, and enforce identity and privileged-access controls. Research confidential computing and hardware roots of trust while documenting technical designs, tests, and security findings.

Supports hardware and platform security for AI infrastructure across firmware, operating systems, and cloud services. Builds Python or Bash automation for telemetry, configuration drift detection, and incident investigations; prototypes AI-assisted security tools; researches confidential computing, remote attestation, and hardware roots of trust; and delivers a mentored security project with documented code and a final presentation.

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.