Principal AI Research Scientist Post-Training · Alignment · Reinforcement Learning Autodesk AI Lab: London · San Francisco · Toronto · Remote (US/CA/EU
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 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 At 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.
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 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 At 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.