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.

Own expert sourcing and candidate pipeline performance across key initiatives. Lead sourcing pods, manage associate capacity and throughput, optimize sourcing channels, build expert relationships, oversee talent pipelines, and partner with Project Management, Product, and Engineering. The role combines hands-on outbound growth work with strategy, systems improvement, prioritization, and team leadership to support revenue growth and scalable delivery.

Build scalable data infrastructure, analytics, dashboards, metrics, and business workflow automations. Partner directly with stakeholders to identify problems, define roadmaps, and deliver production-ready solutions using SQL, dbt, Snowflake, Python, and BI tools. The role requires high autonomy, strong business judgment, rapid learning, AI fluency, and exceptional execution in a fast-paced in-person environment.

Design and maintain benchmarks, metrics, evaluation systems, rubrics, and automated scoring frameworks for language models. Conduct failure analysis of tool use, reasoning, and safety issues; quantify data quality and model performance; and translate findings into improvements for post-training, reward design, data curation, and benchmark development. Collaborate with AI researchers and applied AI teams in a fast-paced, in-person research environment.

Conduct applied AI research on post-training and RLVR for large language models. Design reward-shaping experiments, build scalable synthetic data and augmentation pipelines, develop evaluators and rubrics, and operate large-scale benchmarks and metrics. Analyze data quality and model performance while collaborating with AI researchers and training-data experts. The role requires strong coding, machine learning, algorithms, backend systems, and experimental reasoning skills in a fast-paced, in-person environment.

As a Backend Engineer, you'll build backend services for candidate-job matching and marketplace workflows, focusing on APIs, data models, and operational systems.

Build and operate GPU and accelerator infrastructure across cloud, on-premise, and neocloud environments. Responsibilities include Kubernetes and Slurm orchestration, distributed training, LLM serving, scheduling, benchmarking, observability, automated remediation, cost attribution, autoscaling, and capacity planning. The role writes production code, owns systems end to end, supports on-call operations, and develops tools and dashboards for technical and non-technical users.

Own global payroll delivery across multiple countries, including payroll cycles, reconciliations, statutory filings, vendor governance, controls, and issue resolution. Partner with Accounting, People Ops, Legal, and international providers on journals, close, and compliance. Lead payroll integrations for acquisitions and new-country launches, improve HRIS-to-payroll workflows, build audit-ready procedures, and automate recurring checks and reporting while protecting employee data.

Lead accounting across acquisitions from confirmatory due diligence through post-close audit. Responsibilities include evaluating transaction risks, overseeing purchase price allocations, coordinating Day 1 close readiness, managing accounting integrations, establishing opening balances and ERP mappings, tracking earnouts and goodwill impairment, supporting SOX-compliant reporting, and partnering with executives, legal teams, auditors, and acquired leadership. The role also builds scalable M&A accounting playbooks, governance frameworks, metrics, and team capabilities.

Mercor seeks research proposals examining AI’s economic and labor impacts, human-AI collaboration, productivity, enterprise adoption, and returns. Eligible applicants include researchers at universities, nonprofits, public benefit corporations, and small startups in the United States or United Kingdom. Applicants must submit an expression of interest describing methodology, team expertise, outcomes, timeline, risks, and budget. Selected projects must produce an openly licensed paper, dataset, methodology, or tool.

Own pricing and monetization strategy across AI data products. Build acquisition cost models, rate cards, pricing and packaging frameworks, margin structures, unit economics, and forecasting tools. Establish data valuation and cost ceilings, analyze competitor behavior, attribute revenue and costs, and refine pricing using market and customer feedback. Partner closely with Sourcing, Sales, Finance, and Product in an ambiguous marketplace environment.

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.