Principal Scientist leading research and development for Kraft Peanut Butter in Canada. Collaborating with teams to drive product development and ensure quality standards.
AU
Research Lead – Principal Scientist, Manager Post-Training, Alignment, Reinforcement Learning
Posted last month
About the role
- Leading research in post-training alignment and reinforcement learning at Autodesk AI Lab. Managing a team of AI scientists to develop reliable foundation models for various industries.
Responsibilities
- Own post-training strategy for model development — from RLHF and preference optimization to agentic systems and long-horizon reasoning
- Develop novel algorithms that improve model reliability, controllability, and alignment
- Make principled architectural decisions about when to address challenges at the pre-training, post-training, or system level
- Design and run experiments that shape model behavior, robustness, and reasoning quality
- Partner with infrastructure teams to build scalable, reproducible post-training workflows
- Contribute to publications, patents, and Autodesk's external research visibility
- Design evaluation frameworks for long-horizon reasoning, tool use, agentic behavior, safety, and real-world workflow completion
- Lead rigorous model analysis and interpretability efforts
- Drive human-in-the-loop evaluation with high annotation quality and sound scientific methodology
- Establish model readiness criteria and provide go/no-go recommendations for releases
- Manage, mentor, and grow a team of AI scientists
- Set technical direction and research priorities across post-training and alignment initiatives
- Foster a research culture grounded in scientific rigor, reproducibility, and fast iteration
- Help recruit world-class talent across ML, RL, alignment, and foundation models
- Partner closely with pre-training teams, infrastructure, product organizations, and other stakeholders
- Translate research trade-offs into clear, decision-ready guidance for leadership
Requirements
- Deep hands-on expertise in reinforcement learning for foundation models, and fluency with post-training methods (RLHF, RLAIF, DPO, PPO, or adjacent approaches)
- Proven experience leading or mentoring technical research teams — whether in an academic lab, AI research organization, or industry setting
- Strong intuition for model behavior, alignment challenges, and post-training trade-offs
- Experience designing evaluation systems and thinking rigorously about what it means for a model to be ready
- Ability to communicate complex technical trade-offs clearly to both technical and non-technical audiences
- A PhD or equivalent depth of industry research experience in ML, RL, AI, or a related field
Benefits
- health insurance
- retirement plans
- paid time off
- flexible work arrangements
- professional development
- bonuses
- stock options
- equipment allowances
- wellness programs
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