# Software Engineer, Machine Learning at Mercor

Software Engineer, Machine Learning at Mercor in San Francisco. On-site, full-time, paying $160k–$500k. Posted June 30, 2026.

- Company: Mercor
- Location: San Francisco
- Workplace: On-site
- Job type: Full-time
- Pay: $160k–$500k
- Posted: 2026-06-30
- Apply: https://jobs.ashbyhq.com/mercor/7cee578f-799c-46ad-8951-cb0b724d619a/application
- Source: https://jobmagic.ai/jobs/software-engineer-machine-learning-mercor-bgz67r

## Requirements

- 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

## Description

**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