Director, Enterprise Machine Learning & Research
Scale AI · San Francisco, CA | New York City, NY · Research
About this role
Scale AI is hiring a principal-level AI Research Scientist in the machine learning function based in San Francisco, CA | New York City, NY. The posting calls out experience with LLMs, Deep Learning, Reinforcement Learning, Machine Learning. Compensation is listed at $289,800–$362,250 per year.
- Role
- AI Research Scientist
- Function
- machine learning
- Level
- principal
- Track
- Management
- Employment
- Full-time
- Location
- San Francisco, CA | New York City, NY
- Department
- Research
More roles at Scale AI
Job description
from Scale AI careersThe Enterprise ML team works on the front lines of the AI revolution, partnering deeply with customers to identify high-impact business problems and build cutting-edge AI systems using Scale’s proprietary research, data, and infrastructure—unlocking domain expertise through high-quality data and expert feedback.
As Director of Enterprise ML, 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.
This role is ideal for a leader who thrives in ambiguity, understands both frontier GenAI capabilities and their limitations, and is motivated by turning research into durable, production-ready systems.
What You’ll Do
- 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.
- Communicate compellingly: publish research, present at conferences, engage in open-source contributions, and represent the team externally.
- Drive an inclusive, high-performing culture: help your team through technical challenges, provide growth opportunities, and attract top talent.