Research Engineer - Environments, Data and Post-Training

Навыки

• Data Quality
• LLM
• Machine Learning
• NoSQL
• Ответственность за результат
• SQL

О компании и продукте

• 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.
• As a Research Engineer at Mercor, you’ll work at the intersection of engineering and applied AI research. You’ll contribute directly to post-training and RLVR, synthetic data generation, and large-scale evaluation workflows that meaningfully impact frontier language models.

Задачи

• Work on post-training and RLVR pipelines to understand how datasets, rewards, and training strategies impact model performance
• Design and run reward-shaping experiments and algorithmic improvements (e.g., GRPO, DAPO) to improve LLM tool-use, agentic behavior, and real-world reasoning
• Quantify data usability, quality, and performance uplift on key benchmarks
• Build and maintain data generation and augmentation pipelines that scale with training needs
• Create and refine rubrics, evaluators, and scoring frameworks that guide training and evaluation decisions
• Build and operate LLM evaluation systems, benchmarks, and metrics at scale
• Collaborate closely with AI researchers, applied AI teams, and experts producing training data
• Operate in a fast-paced, experimental research environment with rapid iteration cycles and high ownership

Требования

• Strong applied research background, with a focus on post-training and/or model evaluation
• Strong coding proficiency and hands-on experience working with machine learning models
• Strong understanding of data structures, algorithms, backend systems, and core engineering fundamentals
• Familiarity with APIs, SQL/NoSQL databases, and cloud platforms
• Ability to reason deeply about model behavior, experimental results, and data quality
• Excitement to work in person in San Francisco, five days a week (with optional remote Saturdays), and thrive in a high-intensity, high-ownership environment

Будет плюсом

• Real-world post-training team experience in industry (highest priority)
• Publications at top-tier conferences (NeurIPS, ICML, ACL)
• Experience training models or evaluating model performance
• Experience in synthetic data generation, LLM evaluations, or RL-style workflows
• Work samples, artifacts, or code repositories demonstrating relevant skills

Условия

• Bi-annual performance bonus structure
• Generous equity grant vested over 4 years
• Up to $15k Relocation bonus
• $10K housing bonus (if you live within 0.5 miles of our office)
• $1.5K monthly stipend for meals
• Free Equinox membership
• $200 monthly laundry reimbursement
• $200 monthly personal wellness reimbursement
• Health, Dental, Vision insurance

Паспорт вакансии

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