Level

Mercor

Software Engineer, Applied AI - Frontier Engineering (NYC)

AI in this role

Software Engineer needed to build and operate scalable data pipelines and systems sitting between frontier AI research and data delivery.

ai-researchpythonmachine-learningdata-pipelinessynthetic-dataevaluation-frameworks
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

On the Software Engineer, Applied AI - Frontier Engineering team , you’ll build, deploy, and operate systems that sit directly between frontier AI research and data delivery.

This is a high-ownership, deeply technical role. You’ll work through ill-defined problems, prototype quickly with researchers and customers, and take systems from early experiments to reliable, scalable production. You’ll own projects end-to-end: spanning requirements gathering, creating data creation pipelines, and improving model-adjacent infrastructure, while partnering closely with frontier AI labs and Mercor’s internal teams to ship high-impact applied AI solutions.

What You’ll Do

  • Partner closely with frontier AI labs to understand their data, post-training, and evaluation needs

  • Build and operate scalable data pipelines for post-training workflows and model evaluations

  • Design and build scalable systems for synthetic data generation and data quality, and work directly with customers to understand requirements and develop technical solutions

  • Prototype new data types, benchmarks, and evaluation frameworks

  • Lead technical discussions with customers

What Makes This Role Different

  • Direct frontier exposure. You’ll work closely with researchers at leading AI labs, building infrastructure that directly accelerates cutting-edge research.

  • Coding + customer work. This role blends deep technical execution with customer interaction. Engineers who enjoy both building and communicating tend to thrive.

Day-to-Day

  • Fast-moving, high-ownership environment

  • Technically demanding, collaborative work

  • “Building the plane while flying it”

  • In-person culture at our new New York office at One World Trade Center

  • Aligned with frontier research timelines

What We’re Looking For

  • Strong backend engineering fundamentals in a modern language (Python, Go, Rust, etc.)

  • Experience with model training and inference

  • Strong grounding in statistical analysis and experimental design for measuring model performance and improvements

  • Familiarity with evaluation methods for large language models

  • Comfort working through ambiguity and shipping iteratively

You’re likely someone who

  • Enjoys ownership and customer-facing problem solving

  • Thinks entrepreneurially and moves quickly

  • Balances speed with engineering rigor

  • Communicates clearly with technical and non-technical users

Why Engineers Join

  • Direct exposure to frontier AI research

  • Real ownership and visible impact

  • Technical depth paired with human interaction

  • Fast feedback loops and high leverage

  • There are very few roles that combine this level of technical rigor, customer proximity, and research exposure.

Benefits

  • Bi-annual performance bonus structure

  • Generous equity grant vested over 4 years

  • Up to $15k Relocation bonus

  • $10K proximity 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

Software Engineer, Applied AI - Frontier Engineering (NYC) at Mercor rates 90 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.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

AI ResearchPythonMachine LearningData PipelinesSynthetic DataEvaluation Frameworks

Questions you could be asked

  1. Tell me about a research question you investigated. What did you find?
  2. Tell me about a project where python was part of your work. What did you do?
  3. Tell me about a project where machine learning was part of your work. What did you do?
  4. Tell me about a project where data pipelines was part of your work. What did you do?
  5. Tell me about a project where synthetic data was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: AI Research, Python, Machine Learning, Data Pipelines, and Synthetic Data. 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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