Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

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.