Software Engineer, Machine Learning
At a glance
Mid-level Machine Learning Engineering role at Mercor. San Francisco · full-time · $160,000–$500,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build ranking and matching models that surface candidates and opportunities across the hiring marketplace
- Develop recommendations and personalization, then tune marketplace decisions for fill rate, quality, speed, and conversion
- Create retrieval, scoring, and allocation pipelines for candidate routing and opportunity distribution
- Use hiring outcomes and marketplace feedback to improve models, including when labels arrive late or remain incomplete
- Set up evaluation and experiments that connect model performance to business results
What you bring
- You have shipped machine learning systems into production
- You have worked on ranking, recommendations, search, matching, or marketplace problems
- You can make sound choices about model objectives, evaluation, and tradeoffs
- You can work across data, features, training, inference, and iteration, with strong engineering fundamentals
Who this fits
This role suits an applied machine learning engineer who wants to build product-critical systems and connect model results to marketplace outcomes. The job is based in San Francisco and requires working in person five days a week.
From the employer
About Mercor
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
As a Machine Learning Engineer on the Marketplace team, you will build the models and decision systems that power Mercor's hiring engine. This includes search and ranking, candidate-job matching, marketplace recommendations, personalization, and allocation decisions across a rapidly growing talent network.
This is an applied ML role with direct product and revenue impact. You will work on problems shaped by real marketplace constraints: sparse and delayed labels, cold start, noisy feedback, heterogeneous supply and demand, and the need to optimize across speed, quality, and conversion simultaneously.
What You'll Build
Ranking and matching systems that determine which candidates and opportunities are surfaced
Models for recommendation, personalization, and marketplace optimization
Retrieval, scoring, and decision pipelines operating at global scale
Feedback loops that learn from downstream hiring outcomes, not just top-of-funnel engagement
Real-time and batch inference systems embedded in product-critical workflows
Example Problems
Improve candidate-job matching using embeddings, structured attributes, and behavioral signals
Optimize ranking toward long-term hiring outcomes under delayed and incomplete labels
Design models that balance marketplace objectives such as fill rate, quality, speed, and conversion
Build systems for candidate allocation, opportunity routing, and liquidity optimization
Develop evaluation and experimentation frameworks that connect model performance to business results
What We're Looking For
Strong track record of shipping ML systems into production
Experience with ranking, recommendation, search, matching, or marketplace problems
Good judgment on model design, objective functions, evaluation, and tradeoffs
Comfort working across the full applied ML stack: data, features, training, inference, and iteration
Strong engineering fundamentals and a bias toward simple, robust systems
Why This Role
This role sits on a core decision layer of the product. Your work will directly shape how talent is discovered, matched, and hired, and will influence fundamental marketplace outcomes across quality, speed, and revenue.
Tech Stack
Python, Go, embeddings, fine-tuning, RAG, Kafka, Postgres, Redis, Elasticsearch, Kubernetes, Terraform
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