- Redmond, Washington, United States
- On-site
- Full-time · Senior
- $102K–$219K per year
- 1+ years
Seeking Applied Scientists with solid systems-building skills to push the frontier of large-scale model post-training and adaptation using Microsoft's AI customization platform, Frontier Tuning.
Compensation
The typical base pay range for this role across the U.S. is USD $102,100 - $202,200 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $133,800 - $219,200 per year.
Responsibilities
- Implement methods to adapt foundation models for enterprise-specific tasks
- Contribute to the post-training stack (RL/fine-tuning, architectural adaptation, inference-time steering, tooling)
- Implement and evaluate adaptation approaches under real-world enterprise deployment constraints
- Partner with research and engineering teams to translate product or customer requirements into scalable model adaptation solutions
- Explore post-training techniques that improve domain specialization, tool use, planning, or agentic behaviors
Requirements
- Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
- Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
- Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
- Nice to have: Reinforcement learning or post-training methods