• Actively collaborates with sales and delivery to develop client proposals around process and solution options, create roadmaps, and clearly explain technical concepts for shared understanding to facilitate decision-making • Work as a trusted advisor in an atmosphere of partnership with clients at the sponsor level • Consultatively leads technical and non-technical executive- or sponsor-level client stakeholders on complex technical concepts or design implications • Lead large-scale, complex technical requirements elicitation, design, and scoping discussions, internally and/or with existing and potential clients • Decompose large-scale requirements/user stories into tasks, and accurately estimates the effort for themselves or others to complete those tasks • Demonstrate a consistent capability for effective project leadership on a variety of projects and project types, including multi-workstream large-scale complex projects with global teams • Identifies problems independently and demonstrates proactive ownership of their solution, vets and evaluates others’ proposed solutions, and delegates ownership to others where appropriate • Proactively and professionally raises and articulates risks with the PM, project delivery team, and internal leadership • Productively collaborates with the project team and internal leadership on potential solutions and mitigation approaches, and participates in the related client communication at the sponsor level

• Design, build, and maintain efficient, reusable, and reliable code using TypeScript/Node.js. • Develop back-end services and APIs with Node.js, front-ends with TypeScript/React/Next.js. • Collaborate with other team members and stakeholders. • Optimize applications for maximum speed and scalability. • Identify and fix bugs/performance issues. • Stay updated with emerging technologies/industry trends.

• Developing forecasting interfaces, data pipelines, and inference servers to predict support contact volume. • Designing and implementing interfaces to collect and store team preferences and customer business constraints. • Enhancing machine learning efficiency and operations to support rapid model deployment and iteration.

• Develop RL algorithms that can help with terrain intelligence and social navigation behaviors. • Design, build, and optimize large-scale RL training pipelines (distributed compute, GPU clusters, containerized workflows). • Implement curriculum learning, domain randomization, and multi-agent RL strategies. • Optimize RL model performance, sample efficiency, and stability across thousands to millions of simulation steps. • Build automated tools for experiment orchestration, rollout collection, and metrics visualization. • Develop procedural generation pipelines for synthetic environments, agents, and dynamic behaviors. • Build tools to generate long-tail scenarios, sudden appearance of objects, traffic behaviors, rare events, and environmental variations. • Create systems for configuration, validation, and scoring of generated scenarios. • Collaborate with autonomy, ML, and safety teams to map real-world failures into repeatable synthetic simulation cases. • Design APIs to connect RL agents, scenario generators, planners, and environment simulators. • Debug and optimize simulation performance (real-time speed, determinism, reproducibility). • Work with 3D assets, traffic models, mapping systems (e.g., Isaac Sim, CARLA, Unity, Gazebo). • Partner with autonomy, data, and modeling teams to define training objectives and scenario requirements. • Translate real-world logs and edge cases into parameterized procedural content. • Document tools, frameworks, and workflows for internal users.