About this role
Summary: Leads a team of engineers building evaluation and training infrastructure that improve frontier AI model quality. Owns technical direction for applied AI systems while partnering with product, research, and AI labs.
About Mercor
Mercor’s mission is to organize human intelligence for the AI economy. We build the infrastructure that enables frontier AI labs and enterprises to train, evaluate, and improve AI on real-world professional work. More than five million experts have joined our network, and we pay out over $5 million daily for their knowledge and expertise.
We built our training and evaluation infrastructure alongside the world’s leading AI labs. Now, we’re bringing those capabilities to enterprises across industries, enabling them to train, evaluate, and improve AI using their own expertise and standards. Our APEX benchmarks measure how AI performs on economically valuable work, while our enterprise evaluations and post-training capabilities turn those insights into measurable improvements across real-world workflows.
Our work sits at the intersection of frontier AI, human expertise, and real-world application. We’re solving problems few companies have encountered, with the opportunity to influence what the world’s most advanced AI systems become capable of doing. We’re looking for people who bring different perspectives, take ownership, exercise sound judgment, and hold themselves to an exceptionally high standard. You’ll have the opportunity to do consequential work alongside talented colleagues who challenge one another, collaborate openly, and share a commitment to excellence.
Mercor is profitable and recently raised a $750 million Series D at a $20 billion valuation. We work in person five days a week in San Francisco, New York, and London.
We're hiring Engineering Leads to lead teams within our Applied AI organization.
Applied AI builds the systems that directly determine how fast frontier models improve: evaluation infrastructure, rubric and reward-model pipelines, annotation products, and emerging multimodal capabilities like screen-capture-grounded agent training. This isn't generic ML infra — it's the layer where human expertise gets translated into signal that labs can actually train on. You'll lead a team of engineers, partner closely with product, research, and operations, and help define how Mercor stays at the frontier as the industry shifts from static data labeling to dynamic, verifiable training environments.
You'll also build direct partnerships with leading AI labs and enterprises, and contribute hands-on to improving frontier model quality through data, evaluation, and systems — work that sits squarely in the areas the field agrees matter most right now: RL with verifiable rewards, rubric-based reward modeling for subjective and agentic domains, and the pipelines that turn raw human demonstrations into training-ready environments.
This role requires high ownership and abstraction: setting direction, driving outcomes, and staying hands-on while leading.
What You'll Work On
*Build and scale teams*
- Coach and develop high-potential engineers
- Establish strong ownership, culture, and execution standards
- Shape the team: define hiring processes, establish engineering practices, and scale a high-performing team from the ground up
*Drive Applied AI systems and outcomes*
- Own the methodology behind evaluation systems that benchmark and improve model performance — from rubric design and LLM-judge calibration to reward-hacking mitigation
- Lead development of pipelines that convert AI-moderated interviews and human demonstrations into skills, rubrics, and post-training data at scale
- Drive improvements in data quality, operational efficiency, system stability, and scalability across human-in-the-loop workflows
- Push capabilities up the verifiability spectrum — from cleanly-graded tasks toward the harder, subjective, and agentic work where domain expertise and rubric quality become the bottleneck
*Own technical direction*
- Lead system design for complex AI systems, including multimodal and agentic evaluation pipelines
- Stay close to the technical work and guide engineers through genuinely ambiguous, research-adjacent problems
- Translate emerging research (RLVR, rubric-as-reward, agent grounding from human demonstrations) into concrete engineering roadmaps
*Operate across a broad scope*
- Partner with product, research, operations, and external AI labs on custom evaluation and training-data engagements
- Work across systems, data insights, and lab-specific partnerships to directly move model quality metrics
*Shape the organization*
- Introduce lightweight processes as the organization scales
- Hire and develop strong engineering talent
- Help define what engineering management looks like at Mercor
What We're Looking For
- 6–10 years in engineering; 2–3+ years leading teams
- Strong background in building scalable systems
- Comfort with core ML/LLM concepts — post-training, evaluation metrics, reward modeling — deep enough to partner with researchers, not just consume their output
- Ability to lead in ambiguous, fast-moving environments with sound technical judgment
- Proven ability to coach engineers and drive execution
- Strong ownership mindset with pragmatic decision-making
Mercor is an Equal Opportunity Employer and is committed to providing equal opportunity to all employees and applicants. Mercor does not discriminate based on actual or perceived race, color, creed, religion, national origin, ancestry, citizenship status, age, sex or gender (including pregnancy, childbirth, related medical conditions and lactation), gender identity or gender expression, sexual orientation, marital status, military service and veteran status, disability, protected medical condition as defined by California or local law, genetic information, or any other characteristic protected by applicable federal, California, or local laws and ordinances. Further, Mercor prohibits discrimination, harassment, and retaliation based on any combination of protected characteristics. This Policy applies to all areas of employment, including recruitment, hiring, training, promotion, compensation, benefits, discipline, termination, and all other privileges, terms, and conditions of employment. This Policy and the law prohibit employment discrimination against any employee or applicant based on any legally protected status.