• Provide expert-level consultation on computational aerothermodynamics methodologies, physics-based modeling, and simulation strategies • Advise on applying and integrating machine learning algorithms, including surrogate modeling, physics-informed neural networks, and data-driven turbulence modeling, into CFD and aerothermodynamic workflows • Evaluate and provide feedback on high-performance physical model and numerical method architecture • Assist engineering teams with strategic roadmapping and identification of emerging M&S and computational physics techniques • Serve as a sounding board for senior engineers and developers • Help troubleshoot complex physical and algorithmic challenges

• Build, prototype, and iterate on supervised machine learning models predicting campaign delivery outcomes, including delivery risk, reach, frequency, and contention • Model demand-side campaign outcomes using supply-side signals such as targeting, frequency caps, contention, and pacing • Design offline and online evaluation frameworks for accuracy, robustness, distribution shift, and improvement over the simulation baseline • Own feature engineering and contribute to the team feature store using ad-serving logs, campaign attributes, and supply signals • Ensure model explainability and interpretability for sales and media-planning stakeholders • Partner with ML engineers to deploy models at scale and monitor model health and drift • Collaborate with product, engineering, and sales to define objectives, constraints, and trade-offs and drive adoption of ML-driven forecasts • Communicate technical decisions, trade-offs, and results to technical and non-technical audiences

• Lead the development and improvement of the environment, platform capabilities, and operational foundations supporting machine learning workflows across Oura • Drive the design and unification of workflows and tooling for reliable ML training, orchestration, and deployment • Partner with data scientists and engineers to improve the end-to-end ML lifecycle from experimentation and training through deployment and governance • Define and evolve model governance practices, including reproducibility, lineage, access controls, and operational standards • Standardize ML tooling and workflows such as experiment tracking, model packaging, and promotion processes • Collaborate across business domains to onboard use cases and prioritize shared ML platform improvements • Resolve reliability, scalability, and cost-efficiency issues in cloud ML infrastructure • Drive standards, automation, infrastructure-as-code, CI/CD, and documentation across data science teams • Improve observability and infrastructure automation for ML workflows • Contribute to workflow orchestration and platform integrations for model training and batch inference • Align ML systems with broader data platform and governance practices • Shape best practices for building, shipping, and maintaining production ML systems

• Architect and drive the technical roadmap for a production-grade localization machine learning stack spanning map and sparse landmark-based localization using vision, LiDAR, and radar • Lead research, design, training, and validation of neural architectures for detection, classification, segmentation, tracking, depth estimation, and 3D reconstruction • Drive major feature development from inception to deployment, including architecture design, code reviews, automated testing, mentorship, and technical resolution • Own the end-to-end data strategy, including data curation, auto-labeling, synthetic data, and active learning pipelines • Develop metrics and evaluation frameworks for localization performance and system reliability across diverse Operational Design Domains • Define and validate failure-mode and degradation criteria, safety-case coverage, and graceful fallback behavior • Evaluate and integrate multimodal localization and vision/fusion foundation-model techniques into production solutions • Drive cross-functional alignment and translate autonomy goals into software and system requirements