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

Conduct research on post-training, reinforcement learning with verifiable rewards, data generation, and language model evaluation. Design controlled experiments, develop benchmarks and scoring frameworks, analyze model failures, build research tooling and data pipelines, and communicate findings through reports and presentations. Collaborate with research scientists, engineers, AI teams, and domain experts, contributing to publications, benchmark releases, and frontier AI systems.

Own the architecture and implementation of Applied AI backend systems, including APIs, data models, distributed pipelines, orchestration, infrastructure, and production reliability. Lead technical direction, build high-throughput services, manage containerized infrastructure with Terraform, participate in on-call, resolve incidents, mentor senior engineers, and collaborate across product, operations, and research teams. The role requires strong backend, database, distributed systems, and production operations expertise, plus enthusiasm for agentic AI development.

Build and operate backend services for Mercor’s Applied AI platform, including APIs, data models, background jobs, and high-throughput pipelines. Own features from design through production, with responsibility for reliability, observability, performance, infrastructure, containerized environments, and incident response. Collaborate with product, operations, and research teams to turn ambiguous requirements into production systems.

Build and lead Mercor’s finance-sector client function from the ground up. Identify, onboard, and manage financial services clients; own relationships from scoping through renewal and expansion; negotiate pricing, contracts, compliance, and data-rights terms; and coordinate internal Product, Research, Operations, Legal, and Finance teams. Develop repeatable service playbooks and processes while independently driving outreach, documentation, stakeholder management, and deal closure.

Build end-to-end sensor data pipelines that ingest multi-sensor captures (video, depth, inertial, audio), perform segmentation, pre-labeling, automated QC, privacy redaction, encoding and packaging, and deliver validated, versioned datasets at petabyte scale for frontier AI labs.

Mercor is offering $5 million in grants for research on AI capabilities, including real-world evaluations, interactive environments, long-horizon tasks, agentic systems, reasoning, post-training, and evaluation methodology. Academic groups, independent researchers, and nonprofits may apply with a focused proposal. Applicants should describe their team, background, research accomplishments, project, expected outputs, impact, timeline, and resource needs. Funded projects are expected to produce public research, datasets, methodologies, or tools.

Develop and implement frontier-model post-training methods, including reinforcement learning, verifiable rewards, evaluation, and data-centric optimization. Design rigorous experiments across datasets, rewards, environments, and training strategies; investigate model behavior and failure modes; build scalable data-generation and evaluation pipelines; and create benchmarks, rubrics, and scoring systems. Collaborate with researchers, engineers, AI teams, customers, and domain experts while contributing to open-source tools, technical reports, blog posts, and research papers.

Build and ship production backend systems supporting talent platforms and applied AI data workflows. Interns own projects end-to-end, collaborate with product, design, researchers, and AI companies, and develop scalable systems that impact customers. Responsibilities include writing production-grade code, designing systems, working with APIs and databases, and contributing to AI-focused products in a fast-paced, in-person environment.

Analyze data affecting ranking, hiring efficiency, candidate experience, and revenue. Design experiments, build dashboards and data models, improve data quality, prototype matching and scoring models, and evaluate LLM-powered systems through rubrics and human-in-the-loop studies. Collaborate with engineering, product, and leadership to translate rigorous analysis into product decisions.

Mercor is offering $5 million in grants for independent researchers, academics, PhD students, and small teams conducting frontier AI safety research. Focus areas include misalignment, sandbox escape, evaluation awareness, interpretability, oversight and control, and red-teaming. Funding may support researcher time, API credits, and conference attendance. Applicants should submit a focused expression of interest describing their team, background, research proposal, outputs, impact, timelines, and resource needs.