Manager, Machine Learning Research Scientist, GenAI
Scale AI · San Francisco, CA | Seattle, WA | New York City, NY · Research
About this role
Scale AI is hiring a manager-level Engineering Manager in the software engineering function based in San Francisco, CA | Seattle, WA | New York City, NY. The posting calls out experience with LLMs, Deep Learning, Reinforcement Learning, Machine Learning. Compensation is listed at $398,400–$498,000 per year.
- Role
- Engineering Manager
- Function
- software engineering
- Level
- manager
- Track
- hybrid
- Employment
- Full-time
- Location
- San Francisco, CA | Seattle, WA | New York City, NY
- Department
- Research
More roles at Scale AI
Job description
from Scale AI careersScale AI accelerates the development of AI systems by providing the data, infrastructure, and tooling that power the most advanced models in the world. Our teams operate at the intersection of cutting-edge research, large-scale engineering, and real-world deployment, partnering with leading frontier labs, enterprises, and government agencies to push Generative AI into new capabilities and applications.
As AI rapidly evolves from static models to dynamic, agentic systems, Scale is building the foundational research, evaluation methodologies, and agent/RL infrastructure that will define this next era. You’ll join a high-impact research organization driving advances in large-language models, post-training, evaluation, and agentic/RL environments, helping shape how next-generation AI is built, measured, and deployed.
As a Research Scientist Manager, you will lead a world-class team of research scientists and engineers, define the research roadmap, and drive execution from early prototyping to deployment. You’ll thrive in a fast-moving environment, balancing deep technical leadership with people management, vision setting, and delivery.
You will:
- Lead, mentor and grow a team of research scientists and engineers working on GenAI research initiatives (e.g., evaluation, post-training, agents, RL environments).
- Define and drive a multi-year research roadmap: identify key scientific questions, set milestones, allocate resources, and ensure rigorous execution.
- Collaborate cross-functionally with engineering, product, client-facing teams and external academic or industry partners to translate research into components, insights, and actionable outcomes.