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

Research and develop agentic coding capabilities for frontier AI models. Design reinforcement learning training jobs, coding sandboxes, reward signals, synthetic data pipelines, evaluations, and scalable infrastructure. Analyze large-scale training runs, identify failure modes, and collaborate with infrastructure and post-training teams to ship improvements into model releases.

Conduct AI safety research across data curation, post-training, evaluations, synthetic data generation, and red-teaming. Study how models handle harmful and dual-use requests, develop safety interventions, evaluate long-horizon and agentic behavior, identify jailbreaks and failure modes, and design mitigations. The role requires hands-on Python development, deep learning framework experience, distributed training debugging, and clear technical communication.

Researcher developing and scaling reinforcement learning systems for frontier AI models. Responsibilities include advancing asynchronous RL algorithms, co-designing training recipes and infrastructure, optimizing rollout generation and distributed training, managing large-scale accelerator runs, improving computational efficiency, and conducting rigorous ablations and scaling studies. The role requires full-stack ownership from algorithms through systems implementation and run stability.

Conduct post-training research for frontier AI models by developing and tuning training recipes, designing meaningful evaluations, debugging distributed training, scaling methodologies, and exploring novel datasets. Analyze experimental results, distinguish signal from noise, and publish research, code, datasets, and insights. The role combines theoretical research with high-performance engineering and focuses on alignment, human-AI collaboration, and model usefulness and safety.

Design and optimize distributed infrastructure for large-scale language-model training, focusing on numerical stability, low-precision formats, kernel performance, communication primitives, and parallelism. Build and benchmark systems across multi-GPU and multi-node environments, collaborate on model architectures and training recipes, improve orchestration and monitoring, and share findings through documentation, open-source work, or technical reports.

Supports 3–4 technical leaders by managing calendars, meetings, travel, communications, recruiting coordination, and project commitments. Serves as a key liaison across the company, handles personal logistics, and works autonomously in a fast-changing startup environment. The role requires adapting to different leadership styles, coordinating across time zones, maintaining discretion with sensitive information, and potentially managing a satellite office.

The Post-Training Product Manager partners with frontier AI researchers to set priorities, connect research with product goals, identify dependencies, and guide decisions. Responsibilities include translating ambiguous questions into learning plans, coordinating research and infrastructure stakeholders, incorporating user and model-behavior evidence, supporting release readiness, documenting decisions, and helping turn research outcomes into usable capabilities.

Own the deployment strategy and roadmap for moving trained and fine-tuned AI models into reliable production use. Define serving workflows, autoscaling, monitoring, rollback, incident response, SLAs, pricing inputs, and API/SDK experiences. Partner with infrastructure, research, engineering, GTM, and users to prioritize improvements, resolve dependencies, guide launches, analyze performance and cost tradeoffs, and build a scalable deployment platform.

Leads senior and staff engineers building reliable, performant ML infrastructure and product systems. Owns architecture, system design, technical direction, execution, roadmap prioritization, design reviews, prototyping, and critical issue resolution. Partners with researchers and product teams, hires senior talent, and mentors engineers into technical leaders.

Conduct research and engineering on multimodal AI, focusing on visual perception, vision-language interaction, model architectures, large-scale datasets, evaluation benchmarks, and distributed training. The role involves designing experiments, analyzing model performance, collaborating across research and product teams, publishing findings, and contributing high-performance code to frontier multimodal systems.