Principal AI Research Scientist Post-Training · Alignment · Reinforcement Learning Autodesk AI [...]

Position Overview: Autodesk'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. We publish at NeurIPS, ICML, ICLR, CVPR, and SIGGRAPH. We collaborate with leading academic and industry labs. And we have a direct line from research advances to product impact at scale. This is not a role where research sits behind a wall from engineering — you will see your work matter. 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

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