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

Design, build, and operate identity and access infrastructure at massive scale. Work directly with operations to translate requirements into secure, distributed systems covering authentication, authorization, provisioning, and directory synchronization across thousands of Slack workspaces and HR systems. Own features end-to-end and iterate on design with security-first thinking.

Design, build, and operate backend systems for candidate-job matching, routing, scoring, and marketplace workflows. Develop high-throughput APIs, data models, real-time and async decisioning, and observability tools to ensure reliable, low-latency marketplace performance and scalable fulfillment.

Build and deploy ranking, matching, recommendation, and allocation ML systems for a global talent marketplace. Work across retrieval, scoring, inference (real-time and batch), feedback loops, and experimentation to optimize long-term hiring outcomes under marketplace constraints.

Design and evaluate marketplace pricing, incentives, and allocation mechanisms; measure causal marketplace health; run experiments; forecast supply and demand; partner with ML and engineering to productionize economic objectives and metrics.

Own Mercor's first mobile app from concept to launch and iteration. Define roadmap for experts to apply, track contracts, and submit assessments on mobile. Manage end-to-end lifecycle including specs/PRDs, release management, app store operations, analytics and instrumentation, post-launch growth, and user research. Align product decisions to business outcomes and build scrappy MVPs or dashboards to validate direction.

Scope and size complex AI data projects during pre-sales, drive account growth with strategic customers, lead implementations and integrations, improve delivery reliability, and translate recurring customer needs into product requirements.

Design and improve instructional materials, academies, and internal training systems to upskill talent and employees for complex AI data annotation and evaluation tasks. Create curricula, rubrics, assessments, certifications, and multi-format learning resources while diagnosing and resolving instructional quality issues.

Own major product areas to convert human expertise into training data and tooling for AI. Conduct user research, write specs, build dashboards/MVPs, partner with engineering/research/ops/design, and drive product strategy from conception to execution.

Build end-to-end RL environments and verifiers from enterprise data: ship workflow extraction and grading models, automate task-refinement pipelines, platformize sandbox app clones with real data, deliver data to customers, and scale environment production while preserving high fidelity.

Own end-to-end high-value code-data programs: evaluate frontier coding models, design human/synthetic/model-in-the-loop pipelines, run delivery and QA, manage expert labeling teams, and act as primary customer contact for AI labs.