Research Operations, Code

Mercor|22 Jul 2026

fulltimeonsite

Job Description

About Mercor

Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents.

Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.

About the Role

Silicon Valley's leading AI labs partner with Mercor to build the high-quality code and reasoning data that trains their frontier models. As a member of Research Ops on the Code team, you will own multi-million-dollar data programs end to end — and ownership here is technical as much as operational. You will:

Decompose frontier model capabilities. Reason about where a lab's frontier coding model is weak, and translate that into the data that will make it stronger.
Design the pipeline. Shape how data gets produced — human-expert workflows, synthetic generation, model-in-the-loop systems — not just run a fixed process.
Drive execution. Turn that design into delivery through a team of expert contributors, under demanding timelines, at a consistently high quality bar.
Own the customer. Build deep relationships with lab researchers and become the person they trust to tell them what data they actually need.

This role sits at the intersection of operator and builder. You'll spend as much time reasoning about designing tasks which fail frontier models in fair ways, designing verifiers with parity to production codebases, and constructing self-contained environments which capture real world tasks, as you will on delivery and quality. We operate with startup intensity: occasionally responsive on weekends, always a high bar. The upside is that performance incentives, high-slope career trajectory, and meaningful equity reflects that intensity.

Core Responsibilities

Frontier Model Evaluation & Data Strategy

Develop a working understanding of what our customers' models can and can't do, and where the capability gaps are.
Evaluate model outputs and benchmark performance, and create targeted loss analysis to identify what data will drive improvement.
Translate research goals into concrete task designs, difficulty targets, and quality specifications — including novel frontier tasks that don't exist anywhere else.

Pipeline Design & Innovation

Design how data is produced — human-expert, synthetic, and hybrid model-in-the-loop pipelines — and continuously improve them.
Prototype new generation and validation approaches; bring ideas for new data products, not just improvements to existing ones.
Balance quality, throughput, and cost as you scale a pipeline from prototype to production.

Operational Excellence

Manage end-to-end data pipelines from customer specification to final delivery.
Diagnose bottlenecks, restructure workflows, and implement solutions — incentive systems, workflow re-sequencing, sharper instructions, scaled review processes, and automated quality assurance.
Run daily internal syncs ("war rooms") to stay ahead of issues.

Customer Relationships

Act as the primary point of contact for leading AI labs; deliver clear, consistent reporting.
Proactively anticipate researcher needs and identify opportunities for expansion.

Expert Teams & Workflow Design

Design the human-expert labeling and evaluation flows on the platform — how tasks are structured, reviewed, and scored.
Source, vet, train, and performance-manage teams of domain experts (software engineers, competitive programmers, and specialists).
Maintain a high execution and quality standard across every stage of production.

What We're Looking For

We hire for range: people who can hold a credible technical conversation with a lab researcher in the morning and design the expert workflow that produces the data in the afternoon.

Required

Technical judgment. Coding literacy and genuine ML or Model Benchmark familiarity — enough to evaluate model outputs, reason about frontier-model capabilities and failure modes, read benchmark/eval work, and translate research goals into concrete task and data designs. You don't need to be a research scientist, but you do need to go deep on the technical substance.
Operational ownership. A track record of running complex, high-stakes projects end to end, and genuine energy for large-scale execution and gritty process optimization under pressure.
Communication & customer instinct. Strong analytical and communication skills; comfortable owning high-profile relationships with technical customers.

Bonus — a distinctive spike in one of these stands out

Pipeline building: designing data or automation pipelines; hands-on with LLMs/agents; building synthetic-data or model-in-the-loop systems.
Research fluency: connecting model/benchmark literature to what data would move a frontier model; having created a benchmark or published analysis of model behavior.

Backgrounds that often fit: ML/data/software engineers who love operating, technical PMs, research engineers, or strong generalist operators with real technical range. Backgrounds from consulting, finance, or high-growth startups can work if paired with real technical fluency.

Key Facts

Research Ops team members generate $10M+ in project revenue annually.
Customer requests are high-pressure and time-sensitive (e.g., standing up a vetted expert team and delivering high-quality data against a lab's spec within days).
Day-to-day is roughly ~30% customer engagement, ~40–50% data production (pipelines, experts, scaling), and ~15% operations and metrics review.
This role blends operational ownership with real technical judgment about models and data. It is not a pure project-management seat, nor a pure research seat.
Location: San Francisco (in person, five days a week).