9 hours ago
Base Salary
$130k - $500k/yr
Responsibilities
- Partner with SPLs and client engineering teams to translate data requirements into concrete delivery pipelines.
- Build and operate reliable pipelines moving data from Mercor systems into customer storage at petabyte scale.
- Format and transform datasets according to client specifications, including schemas, field mappings, metadata, versioning, and container formats.
- Build automated validation for delivery pipelines.
- Prototype against new customer requirements and debug issues.
- Convert one-off client requests into reusable components and own delivery infrastructure end to end.
- Lead technical conversations with customers and collaborate with operations, product teams, engineers, and researchers.
Requirements
- Strong backend and data engineering fundamentals in a modern language such as Python, Go, or Rust.
- Comfort operating production systems on AWS and GCP.
- Experience building and owning high-volume data pipelines.
- Experience with large binary formats, streaming ingestion, distributed batch processing, and object storage economics.
- Ability to work through ambiguity, ship iteratively, and manage evolving customer and data-type requirements.
- Customer-facing communication skills, including leading technical discussions and challenging unreasonable requests when needed.
Benefits
- Bi-annual performance bonus structure.
- Generous equity grant vested over four years.
- Up to $15k relocation bonus.
- $10K housing bonus for employees living within 0.5 miles of the office.
- $1.5K monthly meal stipend.
- Free Equinox membership.
- $200 monthly laundry reimbursement.
- $200 monthly personal wellness reimbursement.
- Health, dental, and vision insurance.
- In-person work five days per week in the San Francisco, New York City, or London offices.
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.