• Own AI-powered features end-to-end, from scoping, design and requirements engineering to feature evaluation, deployment and operation. • Design, build and maintain cloud-native, event-driven AI applications across the full stack. • Evaluate, integrate, and optimize AI models for production use (quality, latency, cost). • Build and maintain robust AI inference, evaluation, and monitoring pipelines. • Collaborate closely with product, design, and non-technical stakeholders to identify requirements and turn business needs into value-creating technical solutions. • Operate and improve production systems, taking ownership of reliability, quality, and technical debt. • Plan, track, and break down work effectively in an agile team.

• Design, implement, and optimize Proximal Policy Optimization (PPO) algorithms for domain-specific use cases. • Develop and train reinforcement learning models for real-world applications, focusing on efficiency and scalability. • Collaborate with cross-functional teams to integrate PPO models into production systems. • Analyze model performance and experiment with hyperparameter tuning to achieve optimal results. • Stay up-to-date with the latest research and advancements in reinforcement learning and apply them to enhance existing solutions. • Build robust pipelines for training, evaluation, and deployment of RL models. • Document workflows, methodologies, and code for reproducibility and knowledge sharing.

• Wear different hats across the research-to-production lifecycle: ML Engineer, ML-Ops Engineer, Software Engineer, and Performance Engineer. • Own and evolve our research infrastructure end-to-end, from experiment orchestration and distributed training to model tracking, evaluation, and automated deployment, so researchers can move from idea to validated result quickly. • Build and scale distributed compute for research workloads (e.g. Ray-based training and data pipelines on Kubernetes/GCP), including managing GPU capacity across zones/regions and keeping experiment infrastructure reliable and cost-efficient. • Improve the speed and quality of our R&D through performance engineering: vectorizing and parallelizing simulators and training code, profiling bottlenecks, and driving large speedups. • Deeply understand the capabilities and tools offered by Phaidra’s internal platform and how to utilize them to best serve our customers. • Maintain clear and concise documentation of your research, products and actions. • Participate in making decisions for the medium-to-long-term vision impacting Research and Phaidra. • Mentor peers and delegate tasks within the team, owning the project delivery. • Act as a point of contact between Research and Production engineering teams to productionize new breakthroughs rapidly.

• Contribute to the next generation of AI-powered experiences at Autodesk • Help shape how Autodesk Assistant and our broader AI platform investments create meaningful value for customers across Autodesk products and workflows • Work closely with leaders across product, design, engineering, and data science to uncover customer needs, behaviours, and opportunities • Use customer insights to influence product strategy, roadmap decisions, and the evolution of Autodesk Assistant and agentic experiences • Evaluate the value, usability, trust, and effectiveness of Assistant capabilities and AI-powered experiences across Autodesk products • Establish cross-team connections to coordinate research efforts & evaluation approaches