Autodesk
Technology
PrincipalAIResearchScientistPost-Training·Alignment·ReinforcementLearning
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optimal for Principal candidates.
“Principal AI Research Scientist Post-Training · Alignment · Reinforcement Learning at Autodesk. Skills: AI Research, Reinforcement Learning, Model Alignment. Develop novel algorithms. Make principled architectural decisions”
What You'll Achieve.
Improve model reliability; Improve model controllability; Improve model alignment; Shape model behavior; Shape model robustness; Shape model reasoning quality; Build scalable workflows; Build reproducible workflows; Increase external research visibility; Complete real-world workflows
Industry & Context.
Alignment challenges; Post-training trade-offs; Model analysis; Interpretability efforts
What They're Looking For.
Must Have
Deep hands-on expertise in reinforcement learning for foundation models, Fluency with post-training methods (RLHF, RLAIF, DPO, PPO, or adjacent approaches), Proven experience leading or mentoring technical research teams, Experience designing evaluation systems, Ability to communicate complex technical trade-offs clearly, 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 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
Nice to Have
Specific ML framework experience, Cloud platform certs
What You'll Do.
Develop novel algorithms
Make principled architectural decisions
Design and run experiments
Partner with infrastructure teams
Contribute to publications
Design evaluation frameworks
Lead rigorous model analysis
Drive human-in-the-loop evaluation
Establish model readiness criteria
Provide go/no-go recommendations
Communicate technical risks
How You'll Work.
Team & Collaboration
Technical research teams; Infrastructure teams; Leading teams
Communication Scope
Technical trade-offs; Leadership communication
Full Job Description
**Job Requisition ID #** 26WD98667 **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 an
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