Principal AI Research Scientist Post-training Alignment

Autodesk Autodesk · Enterprise · Toronto, ON +9 · Remote

Autodesk is seeking a Principal AI Research Scientist focused on post-training alignment for foundation models. The role involves developing novel algorithms for model reliability, controllability, and alignment using reinforcement learning, preference optimization, and agentic systems. Responsibilities include designing experiments, building scalable workflows, contributing to publications, and establishing model readiness criteria. The position emphasizes leveraging Autodesk's unique domain-grounded verifiers for RL and requires a strong publication record and experience in alignment research or agentic AI.

What you'd actually do

  1. Post-training for model development — from RLHF and preference optimization to agentic systems and long-horizon reasoning
  2. Develop novel algorithms that improve model reliability, controllability, and alignment
  3. Design and run experiments that shape model behavior, robustness, and reasoning quality
  4. Design evaluation frameworks for long-horizon reasoning, tool use, agentic behavior, safety, and real-world workflow completion
  5. Establish model readiness criteria and provide go/no-go recommendations for releases

Skills

Required

  • Reinforcement learning for foundation models
  • Post-training methods (RLHF, RLAIF, DPO, PPO)
  • Leading or mentoring technical research teams
  • Designing evaluation systems
  • Communicating complex technical trade-offs
  • PhD or equivalent depth of industry research experience in ML, RL, AI
  • Experience at a frontier model lab or advanced applied AI organization
  • Strong publication record
  • Alignment research, preference learning, or agentic AI
  • Deploying or supporting production AI systems
  • Large-scale training infrastructure and compute trade-offs

Nice to have

  • Physics simulation engines
  • CAD kernels
  • Computational design tools
  • Human-in-the-loop evaluation

What the JD emphasized

  • 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
  • Experience designing evaluation systems and thinking rigorously about what it means for a model to be ready
  • A strong publication record at leading ML or AI venues
  • Background in alignment research, preference learning, or agentic AI

Other signals

  • post-training alignment
  • reinforcement learning for foundation models
  • evaluation frameworks for agentic behavior
  • publication record