• 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.

• Translate product ideas, domain concepts, and company vision into intelligent systems that reason over food, ingredients, nutrition, and user preferences. • Design and implement AI-powered features end-to-end, including model selection, orchestration, inference pipelines, confidence handling, and integration with product workflows. • Guide teams through decisions on model tradeoffs, data quality, evaluation strategies, observability, explainability, and long-term system evolution. • Influence without authority — shaping engineering culture, mentoring senior engineers, and raising the organizational bar for how intelligence is built and trusted. • Lead solution design for high-complexity, AI-driven initiatives and deliver production-ready implementations when needed. • Define patterns and standards for safe, observable, and explainable AI usage across teams. • Diagnose and resolve issues across data inputs, model behavior, system integration, and user-facing outcomes. • Mentor senior engineers and elevate engineering culture around intelligent system design. • Partner closely with product, data, CTO, and platform teams to shape roadmap feasibility and long-term intelligence strategy.