Autodesk
Technology
PrincipalAIResearchScientistPost-TrainingAlignment
Neural analysis suggests this role is
optimal for Senior candidates.
“Principal AI Research Scientist Post-Training Alignment at Autodesk. Skills: Post-training alignment, Reinforcement learning, Foundation models, AI research. Develop novel algorithms. Improve model reliability”
What You'll Achieve.
Improve model reliability; Improve model controllability; Improve model alignment; Shape model behavior; Shape model robustness; Shape model reasoning quality; Ensure workflow completion; Achieve high annotation quality; Establish model readiness criteria; Provide go/no-go recommendations
Industry & Context.
Model analysis; Interpretability efforts; Troubleshooting
What They're Looking For.
Must Have
Deep expertise in reinforcement learning, Fluency with post-training methods, Proven experience leading research teams, Experience designing evaluation systems, Ability to communicate technical trade-offs, PhD or equivalent research experience, Experience at a frontier model lab, Publication record at leading ML/AI venues, Background in alignment research, Experience deploying AI systems, Familiarity with large-scale training infrastructure
Nice to Have
Experience with RLHF, Experience with RLAIF, Experience with DPO, Experience with PPO, Experience with agentic systems, Experience with long-horizon reasoning, Experience with tool use, Experience with model interpretability, Experience with human-in-the-loop evaluation, Experience with scientific methodology, Experience with model readiness criteria, Experience with technical risks, Experience with model limitations, Experience with model trade-offs
What You'll Do.
Develop novel algorithms
Improve model reliability
Improve model controllability
Improve model alignment
Make architectural decisions
Address pre-training challenges
Address post-training challenges
Address system level challenges
Shape model robustness
Shape model reasoning quality
Partner with infrastructure teams
Build scalable workflows
Build reproducible workflows
Contribute to publications
Contribute to patents
Design evaluation frameworks
Evaluate long-horizon reasoning
Evaluate agentic behavior
Evaluate workflow completion
Lead interpretability efforts
Drive human-in-the-loop evaluation
Establish model readiness criteria
Provide go/no-go recommendations
Communicate technical risks
Communicate model limitations
Communicate model trade-offs
How You'll Work.
Team & Collaboration
Partner with infrastructure teams; Collaborate with academic labs; Collaborate with industry labs
Communication Scope
Communicate technical risks; Communicate model limitations; Communicate model trade-offs
Process & Methodology
Go/no-go recommendations
Full Job Description
**Job Requisition ID #** 26WD98722 **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. **Responsibilities** * 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 ann
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