Research Scientist

• Own post-training strategy 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
• Manage, mentor, and grow a team of AI scientists
• Set technical direction and research priorities across post-training and alignment initiatives
• Foster a research culture grounded in scientific rigor, reproducibility, and fast iteration
• Help recruit world-class talent across ML, RL, alignment, and foundation models
• Partner closely with pre-training teams, infrastructure, product organizations, and other stakeholders
• Translate research trade-offs into clear, decision-ready guidance for leadership

Research Lead – Principal Scientist, Manager Post-Training, Alignment, Reinforcement Learning

• Research and develop generative and distributional models (e.g., flow matching, diffusion models) to predict high-dimensional cellular responses.
• Build and maintain ML systems capable of processing massive multiomics datasets on high-performance compute clusters.
• Work closely with colleagues to ensure model predictions are interpretable, trustworthy, actionable, and grounded in real experimental outcomes.
• Help design and implement rigorous evaluation metrics that test generalization across for cellular context, unseen perturbations and covariates, going beyond IID performance to reflect real deployment conditions.
• Publish findings in top-tier venues (e.g., NeurIPS, ICML, Nature, Science, Cell) and contribute to the broader scientific community.

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Research Lead – Principal Scientist, Manager Post-Training, Alignment, Reinforcement Learning

Research Scientist, Virtual Cell Modelling, Perturbative Biology