About Mercor

Mercor is training models that predict how well someone will perform on a job better than a human can. We use our platform to source, vet, and onboard expert contractors who help train AI models in a wide variety of domains. Our technology is so effective it’s used by all of the top 5 AI labs.
We scaled from $1-500M in revenue run rate in the last 17 months, making us the fastest growing company in the world. Our growth is accelerating. We averaged 11% week over week growth in July, 18% WoW growth in August, and 19% WoW growth in September. The team is small and we remain profitable because we can’t hire great people as quickly as revenue is growing."

About the Role

As a Machine Learning Engineer at Mercor, you’ll be a key contributor to the backbone of our core product: models and systems that power talent discovery, evaluation, and trust. From fraud detection to candidate-job matching to performance prediction, your work will directly impact how companies find talent and how people land their next opportunity.

This role blends generalist backend engineering with applied machine learning and statistical modeling. You’ll work across the stack: designing and deploying models, building scalable infrastructure, and collaborating with product and operations teams to solve real-world problems.

What You’ll Work On

• Researching, training, and deploying ML models in production (e.g., fraud/cheating detection, candidate engagement and conversion prediction, search/recommendation).

• Building backend infrastructure and APIs to serve ML models at scale.

• Collaborating with product engineers, sourcing/operations, and other teams to align models with business impact.

• Running experiments, analyzing results, and iterating quickly to improve model and product performance.

• Wearing multiple hats, sometimes data scientist, sometimes backend engineer, always problem solver.

What We’re Looking For

• Strong background in backend engineering (Python/Django or similar).

• Solid foundation in machine learning and statistics; experience applying ML in production.

• Comfort with ambiguity and a willingness to work across domains.

• Familiarity with LLMs, search/recommendation systems, or classification models is a bonus.

• Above all: a strong generalist engineer with the curiosity and rigor to tackle diverse problems.

Why Join Us

• Work on high-impact problems at the intersection of ML and talent discovery.

• Own projects end-to-end in a fast-moving, high-autonomy environment.
  Collaborate with a team of versatile engineers who are comfortable wearing many hats.

• Contribute to a mission of empowering a billion people to find their next job.