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

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