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

Build and own UI features for Netflix’s TV application across streaming sticks, smart TVs, and cable boxes. Collaborate with product, design, algorithm, and engineering teams to create and refine consumer experiences. Develop TypeScript and JavaScript features using React and Redux, optimize for performance- and memory-constrained devices, conduct A/B testing, and write automated tests using Jest-based frameworks.

Owns product operations for the Content Experience Innovation team, including operating rhythms, OKRs, QBRs, portfolio visibility, roadmaps, tooling, workflows, and leadership communications. Leads cross-functional innovation programs across Product, Engineering, Design, Content, and other teams, improving efficiency through scalable processes, AI, and automation. Partners with senior leadership to shape transformation initiatives and align product development with broader business goals.

Owns end-to-end human evaluation programs for Netflix recommendation quality. Partners with product, ML, data science, UX, engineering, and vendors to design evaluation frameworks, rubrics, metrics, and workflows. Manages annotation capacity, SLAs, costs, rater onboarding, calibration, quality assurance, and delivery timelines. The role also improves operational efficiency, resolves blockers, and translates recommendation strategy into scalable evaluation programs.

Build and maintain data models, pipelines, metrics, dashboards, and analytics supporting Netflix’s advertising identity graph, entity matching, and identity resolution systems. Evaluate match quality, coverage, graph stability, and data drift; collaborate with engineering, data science, product, and identity providers; and communicate technical insights and trade-offs to technical and non-technical stakeholders.

Lead ML initiatives to improve Sales productivity and automation: define roadmap, design and deploy production ML models and AI agents, build measurement pipelines with analytics engineers, iterate from performance and seller feedback, and act as senior technical partner across product, engineering, and Sales to drive measurable revenue impact.

Build AI-augmented gameplay and interactive storytelling systems using generative AI. Integrate LLMs and diffusion models with game engines, behavior trees, narrative systems, and world-building tools. Develop guardrails for safe, balanced, on-theme content; create tools that accelerate prototyping and playtesting; and troubleshoot AI interactions with deterministic game logic. Partner closely with designers to deliver fun, high-quality player experiences with low-latency performance.

Lead research and end-to-end development of machine learning solutions involving LLMs, generative models, summarization, LLM-based evaluation, and recommendation systems. Translate advances in generative AI into business applications, optimize generated content for member impact, partner with engineering and ML teams, define technical roadmaps, and communicate complex concepts to stakeholders.

Leads product strategy, discovery experiences, roadmaps, experimentation, and lifecycle delivery for Netflix’s emerging content formats. Partners across product, engineering, design, merchandising, consumer insights, and data science to identify opportunities, align stakeholders, launch features, measure impact, and iterate. The role focuses on creating unified experiences for content such as games, live events, sports, podcasts, and other emerging formats across Netflix platforms.

Netflix is hiring a Product Manager for its Compute organization. The provided posting contains no specific responsibilities or qualification details. Compensation is an annual salary ranging from $660,000 to $1,000,000, with flexibility between salary and stock options, plus comprehensive benefits.

Design and deploy production machine learning models and conversational AI agents that transform advertiser briefs into optimized media plans. Build evaluation systems to improve model and agent quality, collaborate across science and engineering teams, investigate ambiguous problems, and iterate using performance data and seller feedback. Communicate effectively with technical and non-technical stakeholders while contributing broadly across machine learning domains.