engineering
Posted 1 weeks agoSoftware Engineer, AI Data & Evaluation
at Mercor
San Francisco, United StatesOn-site
$15,000
Responsibilities
- You will design and develop the evaluation methods and flywheels that drive continuous model improvement, engineer synthetic data pipelines and environments that generate high-signal training data at scale, and build the operational automation that keeps it all running with precision and efficiency.
- - Design and build synthetic data generation systems and simulation environments that produce high-signal, high-diversity training data for frontier AI models.
- - Architect and ship operational automation systems that maximize throughput, efficiency, and quality across the end-to-end data pipeline.
- - Collaborate cross-functionally with Operations, Research, and Product to translate evolving model needs into robust, scalable engineering solutions.
- - Own end-to-end delivery of critical systems — from prototyping novel ideas to scaling production infrastructure.
Requirements
- ABOUT MERCOR Mercor's mission is to organize human intelligence to power the AI economy.
- We partner with leading AI labs and enterprises to provide the human intelligence essential to AI development.
- Our vast talent network trains frontier AI models in the same way teachers teach students: by sharing knowledge, experience, and context that can't be captured in code alone.
- Mercor is creating a new category of work where expertise powers AI advancement.
- You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society.
- ABOUT THE ROLE As a Senior Software Engineer (AI Data & Evaluation) at Mercor, you will be at the core of building the data infrastructure and evaluation systems that power the next generation of frontier AI models.
- Our team's mission is to develop high-quality data types that push frontier models forward and drive the AI industry ahead.
- This role demands a product- and impact-oriented mindset, a bias toward shipping, and the ability to thrive at the intersection of data engineering, systems design, and applied AI research.
- experience with AI/ML data pipelines, evaluation frameworks, or training data systems. - Systems thinking: ability to design for scalability, quality, and operational reliability simultaneously. - Comfort operating with ownership and pragmatism in fast-moving, ambiguous environments. - Effective communication and collaboration with engineering, research, and operations teams. -