6 months ago
Base Salary
$130k - $500k/yr
Responsibilities
- Partner with frontier AI labs to understand data, post-training, and evaluation needs.
- Build and operate scalable data pipelines for post-training workflows and model evaluations.
- Design and build systems for synthetic data generation and data quality.
- Work directly with customers to understand requirements and develop technical solutions.
- Prototype new data types, benchmarks, and evaluation frameworks.
- Lead technical discussions with customers.
- Own projects end-to-end from requirements gathering through production delivery.
Requirements
- Strong backend engineering fundamentals in a modern language such as Python, Go, or Rust.
- Experience with model training and inference.
- Strong grounding in statistical analysis and experimental design for measuring model performance and improvements.
- Familiarity with evaluation methods for large language models.
- Ability to work through ambiguity and ship iteratively.
- Comfort with customer-facing problem solving and communicating with technical and non-technical users.
Benefits
- In-person work five days per week in San Francisco, New York City, or London.
- Bi-annual performance bonus structure.
- Generous equity grant vested over four years.
- Up to $15,000 relocation bonus.
- $10,000 housing bonus for living within 0.5 miles of the office.
- $1,500 monthly meal stipend.
- Free Equinox membership.
- $200 monthly laundry reimbursement.
- $200 monthly personal wellness reimbursement.
- Health, dental, and vision insurance.
Categories
About Mercor
Mercor builds an expert-powered platform that trains, evaluates, and deploys AI systems for AI labs and enterprises. It operates APEX to assess model performance on economically valuable tasks, and provides services such as custom AI agents and teams of vetted domain experts who encode organizational knowledge into AI. Founded in 2023 and headquartered in San Francisco, it is privately held and works with frontier AI labs and enterprise clients requiring strict data isolation.