Principal AI Research Scientist Post-Training · Alignment · Reinforcement Learni

Job Requisition ID #26WD98667Position 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.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.RespoinsibilitiesPost-training for model development — from RLHF and preference optimization to agentic systems and long-horizon reasoningDevelop novel algorithms that improve model reliability, controllability, and alignmentMake principled architectural decisions about when to address challenges at the pre-training, post-training, or system levelDesign and run experiments that shape model behavior, robustness, and reasoning qualityPartner with infrastructure teams to build scalable, reproducible post-training workflowsContribute to publications, patents, and Autodesk's external research visibilityDesign evaluation frameworks for long-horizon reasoning, tool use, agentic behavior, safety, and real-world workflow completionLead rigorous model analysis and interpretability effortsDrive human-in-the-loop evaluation with high annotation quality and sound scientific methodologyEstablish model readiness criteria and provide go/no-go recommendations for releasesCommunicate technical risks, limitations, and trade-offs clearly to leadershipMinimum RequirementsDeep 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 settingStrong intuition for model behavior, alignment challenges, and post-training trade-offsExperience designing evaluation systems and thinking rigorously about what it means for a model to be readyAbility to communicate complex technical trade-offs clearly to both technical and non-technical audiencesA PhD or equivalent depth of industry research experience in ML, RL, AI, or a related fieldExperience at a frontier model lab or advanced applied AI organizationA strong publication record at leading ML or AI venuesBackground in alignment research, preference learning, or agentic AIExperience deploying or supporting production AI systemsFamiliarity with large-scale training infrastructure and compute trade-offsAt Autodesk, we're building a diverse workplace and an inclusive culture to give more people the chance to imagine, design, and make a better world. Autodesk is proud to be an equal opportunity employer and considers all qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender, gender identity, national origin, disability, veteran status or any other legally protected characteristic. We also consider for employment all qualified applicants regardless of criminal histories, consistent with applicable law.Are you an existing contractor or consultant with Autodesk? Please search for open jobs and apply internally (not on this external site). If you have any questions or require support, contact Autodesk Careers.Originally posted on Himalayas