MercorEngineering Manager, Applied AI
Mercor is hiring an Engineering Manager, Applied AI in San Francisco, United States. It pays $250k-$400k a year and Level rates it ; you can apply on Mercor.
Level's read on this job
- On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include Apex, engineering management and evaluation infrastructure.
- Level classifies it as a lead role.
- Level lists 88 other open roles at Mercor.
AI in this role
Engineering Manager leading Applied AI teams to build evaluation infrastructure, annotation products, and multimodal frontier AI systems.
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.
About the Role
We’re hiring Engineering Managers to lead teams within our Applied AI organization.
Applied AI builds systems that directly improve model quality, including evaluation infrastructure, annotation products, and emerging multimodal capabilities. You’ll lead a team of engineers, partner with product and research, and help define how Mercor stays at the frontier of AI.
You will also build partnerships with leading AI labs and directly contribute to improving the quality of frontier models through data, evaluation, and systems.
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
Manage and grow a team of 6–10 engineers
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
Drive insights and methodology behind evaluation systems that benchmark and improve model performance
Lead development while driving improvements in data quality, operational efficiency, system stability, and scalability
Scale products that generate high-quality training data and improve human-in-the-loop workflows
Own technical direction
Lead system design for complex AI systems
Stay close to the technical work and guide engineers through ambiguous problems
Translate high-level AI goals into clear engineering roadmaps and execution plans
Operate across a broad scope
Partner with product, research, operations, and external AI labs
Work across systems, data insights, and custom partnerships to drive model quality
Shape the organization
Introduce lightweight processes as the organization scales
Hire and develop strong engineering talent
Help define engineering management at Mercor
What We’re Looking For
Bachelor’s Degree in Computer Science or a related technical degree such as Electrical Engineering, Physics, Mathematics or Statistics
6–10 years in engineering; 2–3+ years managing teams
Strong background in building scalable systems
Ability to lead in ambiguous environments with sound technical judgment
Proven ability to coach engineers and drive execution
Strong ownership mindset with pragmatic decision-making
Who Thrives Here
We’re looking for engineering leaders who take ownership in ambiguous environments, make pragmatic decisions, and consistently deliver measurable impact.
Success Metrics
Build high-performing, well-supported teams
Hire and retain strong engineers
Improve team velocity and execution quality
Contribute to systems that measurably improve model performance
Location
San Francisco (preferred) or New York
Relocation assistance will be provided as needed
Why This Role
Direct impact on frontier AI development
Build partnerships with leading AI labs and shape how frontier models improve
High ownership across team, systems, and hiring
Opportunity to shape a rapidly scaling organization
Work at the intersection of engineering, product, data, and AI
Benefits
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
How we rate this
Engineering Manager, Applied AI at Mercor rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where engineering management was part of your work. What did you do?
- Tell me about a project where evaluation infrastructure was part of your work. What did you do?
- Tell me about a project where annotation was part of your work. What did you do?
- Tell me about a project where multimodal was part of your work. What did you do?
- Tell me about a project where ai systems was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Engineering Management, Evaluation Infrastructure, Annotation, Multimodal, and AI Systems. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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