Mercor
Software Engineer, Applied AI
About this role
Mercor seeks a Software Engineer for Applied AI to build and operate systems connecting frontier AI research with data infrastructure. You'll develop scalable pipelines, work directly with AI labs on post-training workflows, and partner with customers to ship high-impact solutions in a fast-paced, high-ownership environment.
What you'll do
- Partner with frontier AI labs to understand 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 data quality assurance
- Prototype new data types, benchmarks, and evaluation frameworks
- Lead technical discussions and gather requirements directly from customers
- Take projects from early experiments through reliable, scalable production deployment
What they're looking for
- Backend engineering in Python, Go, Rust, or similar modern language
- Model training and inference experience
- Statistical analysis and experimental design
- Large language model evaluation methods
- Data pipeline design and implementation
- Scalable systems architecture
- Clear technical communication with diverse stakeholders
- Comfort with ambiguity and iterative shipping
Benefits
- Bi-annual performance bonus structure
- Generous equity grant vested over 4 years
- Up to $15,000 relocation bonus
- $10,000 proximity bonus for living within 0.5 miles of office
- Access to cutting-edge frontier AI research
- In-person culture at One World Trade Center in NYC
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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
- Describe your experience building data pipelines at scale and what challenges you've faced with data quality and throughput.
- Walk us through a time you worked on an ambiguous problem with incomplete requirements—how did you approach it?