AI Scientist Intern developing and implementing models for healthcare solutions. Collaborating with AI teams to drive data - driven decision making.
About the role
- AI Scientist Manager at Autodesk Research conducting advanced AI research and leading model alignment efforts. Managing a team of AI scientists and ensuring model readiness for production.
Responsibilities
- Lead and contribute directly to post-training pipelines, including: instruction tuning and multi-task fine-tuning; preference optimization (RLHF, RLAIF, DPO, PPO, and related methods)
- Design and run experiments that shape model behavior, robustness, and reliability
- Decide what problems are best addressed through post-training vs pre-training vs product-level mitigation
- Partner with infrastructure teams to ensure efficient, reproducible, and scalable post-training workflows
- Design and maintain evaluation frameworks that measure: long-horizon reasoning and planning; tool-use and agentic behavior; safety, robustness, and alignment; regression and behavioral drift across releases
- Lead human-in-the-loop evaluation, ensuring annotation quality, consistency, and bias awareness
- Provide clear go / no-go recommendations for model releases, including explicit articulation of known risks and trade-offs
- Manage, mentor, and grow a team of AI scientists working on post-training and alignment
- Set clear technical direction while empowering researchers to own end-to-end projects
- Hire and develop scientists with strengths across ML, RL, evaluation, and human-centered AI
- Foster a culture of: rigorous experimentation and ablation, reproducibility and scientific integrity, thoughtful risk-taking and humility about model behavior
- Provide regular feedback, career coaching, and performance management
- Act as a key interface between: pre-training research; infrastructure and compute teams; Model Delivery team; safety, policy, and legal stakeholders
- Translate complex research trade-offs into clear, decision-ready guidance for leadership
- Influence the broader AI roadmap by identifying post-training opportunities that unlock product impact
Requirements
- PhD or equivalent industry experience in Machine Learning, AI, or a related field
- Proven experience as a people manager of technical research or ML teams
- Strong hands-on expertise in: large language models or foundation models, fine-tuning and post-training methods (e.g., RLHF, DPO, instruction tuning), experimental design and evaluation
- Ability to move fluidly between research depth and organizational leadership
- Strong communication skills, with the ability to explain complex trade-offs to technical and non-technical audiences.
Benefits
- health and financial benefits
- time away
- everyday wellness
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