Job Requisition ID #26WD98667
Position OverviewAutodesk's domains - architecture, engineering, construction, manufacturing, media & entertainment - provide a distinctive research environment: rich structured data, long-horizon reasoning tasks, and real-world evaluation grounded in professional workflows. Uniquely, decades of investment in physics simulation engines, CAD kernels, and computational design tools give us something most labs don't have: high-fidelity, domain-grounded verifiers that can serve as reward signals for post-training. Rather than relying solely on human preference data, we can ground reinforcement learning in the laws of physics and the constraints of real engineering. These are exactly the kinds of challenges - and assets - that make post-training and alignment research here genuinely distinctive.
Respoinsibilities- Post-training 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
- Communicate technical risks, limitations, and trade-offs clearly to leadership
Minimum 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
- Experience at a frontier model lab or advanced applied AI organization
- A strong publication record at leading ML or AI venues
- Background in alignment research, preference learning, or agentic AI
- Experience deploying or supporting production AI systems
- Familiarity with large-scale training infrastructure and compute trade-offs
