Mercor
Software Engineer, Applied AI
About this role
Build and operate scalable data pipelines and infrastructure that connects frontier AI research with production systems. This high-ownership role blends deep technical work with direct collaboration on cutting-edge AI post-training, evaluation, and synthetic data generation projects.
What you'll do
- Partner with frontier AI labs to understand their data, post-training, and evaluation requirements
- Build and operate scalable data pipelines for post-training workflows and model evaluations
- Design systems for synthetic data generation and ensure data quality standards
- Prototype new data types, benchmarks, and evaluation frameworks
- Lead technical discussions and gather requirements from customers
- Take systems from early prototypes to reliable, scalable production
What they're looking for
- Backend engineering (Python, Go, Rust, or modern equivalent)
- Model training and inference
- Statistical analysis and experimental design
- LLM evaluation methods
- Scalable data pipeline design
- Customer communication and problem-solving
- Iterative shipping in ambiguous environments
- Data quality and benchmarking
Benefits
- Bi-annual performance bonus
- Generous equity grant vested over 4 years
- Up to $15k relocation bonus
- $10k proximity bonus for living within 0.5 miles of office
- Direct exposure to frontier AI research
- In-person culture at One World Trade Center, NYC
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
Mercor
Mercor builds a marketplace platform connecting expert talent to AI opportunities, supported by identity infrastructure, matching algorithms, and internal tools for data management. The company is hiring Software Engineers, Machine Learning Engineers, Fullstack Engineers, and Security Engineers to develop backend systems, ML models, cloud infrastructure, and distributed platforms.
- Website
- mercor.io
Likely interview questions
- Tell us about a time you built a data pipeline from scratch—what were the biggest challenges and how did you optimize it?
- How have you approached evaluating or benchmarking model performance in a previous role?