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

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.

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.

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.

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.

Own benchmark and evaluation prioritization, coordinate training and inference partners, forecast capacity, manage budgets and SLAs, track customer delivery, and lead operational reviews. Translate recurring delivery issues into product requirements and partner with Engineering to automate workflows. The role requires technical fluency in LLM evaluation, post-training methods, inference economics, SQL, and Python, plus strong project management, vendor judgment, communication, and ownership.

Own Mercor’s APEX AI evaluation product, including roadmap, benchmark portfolio, public and private leaderboards, evaluation integrity, and end-to-end infrastructure. Partner with research, engineering, operations, GTM teams, frontier AI labs, and enterprise customers to develop trusted benchmarks, communicate results, drive adoption, and connect evaluation demand to business impact. Write product specifications, analyze evaluation failures, and improve platform capabilities hands-on.

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.

Design, implement, and operate large-scale evaluation and benchmarking pipelines for LLMs. Build rubrics, automated scorers, dashboards, and failure-analysis workflows to quantify model errors, guide data curation, and inform training and reward-design decisions. Collaborate with researchers, applied-AI teams, and data producers to prioritize and scale evaluations aligned with product and research goals.

The Fullstack Software Engineer will develop and manage the Studio platform's frontend and backend, ensuring reliable data workflows and user-friendly interfaces. Responsibilities include leading engineering best practices, collaborating on technical solutions, and shipping impactful features for AI data generation and delivery.

Design and operate cloud infrastructure security for multi-tenant environments: architect multi-account AWS isolation, harden Kubernetes, deploy CSPM, enforce IaC guardrails, implement least-privilege IAM across platforms, and build incident response and logging pipelines for enterprise customers.