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

Build training systems for Tinker, including reinforcement learning systems, numerics, kernels, data pipelines, and post-training engines. Develop frontier model customization techniques, co-design RL algorithms, debug large-scale training runs, optimize pipelines, and collaborate with internal researchers, companies, and open-science partners to improve platform performance and adoption.

Build and improve Tinker, a fine-tuning API, by developing production-quality Python code, training recipes, integrations, documentation, and developer tools. Work directly with researchers and open source developers to debug technical issues, optimize LLM fine-tuning and deployment workflows, and translate user feedback into product and infrastructure improvements. Share post-training research through tutorials, blogs, talks, demos, and open source contributions while helping shape the developer experience roadmap.

Own full-cycle recruiting for go-to-market roles, including account executives, sales leaders, solutions engineers, partnerships, customer success, and revenue operations. Partner with GTM leadership on role definition, sourcing, assessment, interview design, candidate closing, and hiring processes. Build talent pipelines, evaluate sales capabilities, deliver strong candidate experiences, track recruiting metrics, and improve recruiting tools, workflows, compensation, and leveling guidance.

Own compute allocation for shared supercomputing resources, balancing research demand, workload requirements, hardware suitability, capacity, timing, and dependencies. Partner with researchers and infrastructure engineers to improve scheduling, placement, utilization, and capacity planning. Teach teams to estimate compute needs, explain allocation tradeoffs, develop guidance and tools, and improve decisions through usage and performance analysis.

Operate and scale production inference systems serving live, multi-tenant traffic. Own model rollouts, observability, capacity planning, incident response, reliability, failover, and cost optimization. Partner with research and inference teams to productionize serving techniques while maintaining system performance and resilience.

Design, build, and operate distributed infrastructure for large-scale AI model training and inference. Responsibilities include orchestration, scheduling, storage, resource management, reliability, observability, distributed failure debugging, and developing internal Python and Go libraries and APIs. The role partners with researchers and platform engineers and supports infrastructure operating across thousands of accelerators.

Support 2–3 executives across legal, operations, and communications by managing calendars, meetings, travel, communications, action items, recruiting coordination, company events, and leadership projects. The role requires autonomy, discretion, adaptability, and proactive support in a fast-moving AI startup environment. The position is based in San Francisco and includes coordination across teams and time zones.

Build developer-facing tools, integrations, documentation, demos, and machine learning recipes for Inkling and Tinker. Collaborate directly with open source developers and researchers to debug issues, gather feedback, improve products, and shape the developer experience roadmap. Represent the company through technical writing, talks, tutorials, livestreams, conferences, and meetups. Requires strong Python engineering skills and hands-on experience fine-tuning, evaluating, or deploying large language models.

Build and improve Tinker’s developer experience by creating cookbook recipes, documentation, tools, integrations, demos, and library features. Work directly with users to debug fine-tuning pipelines, optimize workflows, identify product improvements, and influence the platform roadmap. The role also involves sharing technical learnings through blog posts, guides, talks, and code examples while collaborating with research and infrastructure teams.

Own onboarding and early employee lifecycle operations, employment compliance, immigration case management, HRIS and payroll systems, benefits administration, people programs, documentation, and cross-functional support. Build scalable infrastructure, playbooks, dashboards, and processes for a rapidly growing AI company while exercising discretion with sensitive employee information and making sound decisions in an ambiguous environment.