Principal AI Research Scientist Post-Training Alignment
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Key skills for this role
About the Role
Autodesk seeks a Principal AI Research Scientist to lead post-training and alignment research for foundation models. The role involves developing novel algorithms for RLHF, preference optimization, and agentic systems, and collaborating with infrastructure teams to build scalable workflows.
Key Skills for This Role
Responsibilities
- Lead 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
Requirements
- 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
- Strong intuition for model behavior, alignment challenges, and post training trade offs
- Experience designing evaluation systems and thinking rigorously about model readiness
- Ability to communicate complex technical trade offs clearly to both technical and non technical audiences
- 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 strong 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
Full Job Posting
Position Overview
- Autodesk's domains — architecture, engineering, construction, manufacturing, media & entertainment — provide a distinctive research environment with rich structured data, long horizon reasoning tasks, and real world evaluation grounded in professional workflows.
- Decades of investment in physics simulation engines, CAD kernels, and computational design tools give high fidelity, domain grounded verifiers that can serve as reward signals for post training.
- The role involves publishing at NeurIPS, ICML, ICLR, CVPR, and SIGGRAPH, and collaborating with leading academic and industry labs.
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 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
Minimum Requirements
- 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 — whether in an academic lab, AI research organization, or industry setting
- Strong intuition for model behavior, alignment challenges, and post training trade offs
- Experience designing evaluation systems and thinking rigorously about what it means for a model to be ready
- Ability to communicate complex technical trade offs clearly to both technical and non technical audiences
- 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 strong 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
Salary Transparency
- For Canada based roles, we expect a starting base salary between CAD 0 and CAD 0. Offers are based on the candidate’s experience and geographic location, and may exceed this range.
- Compensation package may include annual cash bonuses, commissions for sales roles, stock grants, and a comprehensive benefits package.
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