Jobs at Mercor

The Strategic Project Lead will drive execution of multi-million-dollar projects, optimize processes, and manage a large team while maintaining client relationships.

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.

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.

Lead the technical strategy and development of multimodal data products spanning image, video, audio, documents, processing, review, validation, versioning, and delivery. Design reliable, traceable, cost-efficient distributed systems, remain hands-on with production code, and manage engineering staff through hiring, coaching, delegation, and performance development. Partner with product, ML, operations, and customer teams to build reusable capabilities and operate complex workflows at scale.

As an Infrastructure Engineer, you'll design and maintain cloud infrastructure, build automation workflows, manage CI/CD pipelines, and improve system reliability while collaborating with product and research teams.

Build, deploy, and operate applied AI systems connecting frontier research with data delivery. Develop scalable data pipelines, synthetic data generation systems, quality workflows, model evaluation frameworks, benchmarks, and post-training infrastructure. Partner with AI labs and customers to define requirements, prototype solutions, lead technical discussions, and deliver reliable production systems. The role requires strong backend engineering, experience with model training and inference, statistical analysis, experimental design, and familiarity with large language model evaluation methods.

Build and maintain scalable backend platform systems supporting contracts, payouts, fraud prevention, identity, and access management. Develop reliable infrastructure, improve security and developer experience, translate operational needs into backend services, and contribute across high-priority platform initiatives. The role requires strong ownership, architectural judgment, and the ability to deliver practical systems amid ambiguity.

Design and build backend infrastructure for long-running, stateful workflows involving automated processing, AI model inference, and expert review. Develop orchestration primitives such as retries, recovery, idempotency, and auditable state transitions. Create APIs and tooling for operations, debugging, observability, and reliability. Partner with product, operations, and ML teams to translate ambiguous requirements into scalable, debuggable systems and own services through design, deployment, and continuous improvement.

Build and scale agentic products, LLM agents, harnesses, verifiers, reinforcement learning environments, orchestration pipelines, and full-stack interfaces. Develop Python services and data infrastructure, instrument agent behavior, create evaluations, detect failure modes, and improve reliability, cost, latency, and user experience. Partner with research, product, design, and operations teams to define agent autonomy and human-in-the-loop workflows. The role is fully in-person five days weekly in San Francisco, New York City, or London.

Run end-to-end interview operations for Deeptune: scheduling, candidate experience, Ashby data integrity, on-site and event logistics, reporting, and automations to reduce repetitive work.

The Engineering Manager will lead a team in Applied AI, focusing on building scalable systems, driving evaluations to improve model performance, and managing engineering talent.

The Enterprise AI Associate will engage with customers, support AI solution deployment, and translate feedback into structured plans while collaborating with engineering teams and refining marketing strategies.

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.

As a Software Engineer, Applied AI, you'll architect and implement integrations and scalable solutions for enterprise clients, focusing on technical requirements and business goals.

Build systems for Mercor's hiring engine, including pipelines for indexing, sourcing, scoring, and conversion. Work with ML and backend technologies.

Lead a customer-facing applied AI engineering team to deliver production-grade, cloud-native AI systems. Translate customer workflows into scalable agentic solutions, oversee deployments, evaluation and continual-learning loops, hire and mentor engineers, remove blockers, and coordinate with Sales, Product, Research, and Platform teams to ensure reliable, scalable enterprise deployments and measurable commercial outcomes.

The Enterprise AI Lead will manage relationships with clients, develop commercial strategies, drive initiatives across teams, and present to executives, requiring strong communication and problem-solving skills.

Own end-to-end data delivery and operations for RL training environments: scope projects, manage distributed contributor teams, meet throughput and quality targets, partner with engineers to unblock pipelines, drive process improvements, and deliver high-pressure client requests on tight timelines.

Own full-cycle recruiting for Deeptune and adjacent teams: source through close, partner with hiring managers to define roles, build top-of-funnel, coach interviewers, operationalize hiring systems and metrics (Ashby), drive fast, high-quality hiring, and represent Mercor externally.