• Architect and build large-scale ML systems spanning data, training, evaluation, inference, and deployment. • Design reproducible, high-performance training pipelines across GPU infrastructure. • Architect inference systems that balance latency, throughput, cost, and reliability at scale. • Design and maintain data systems for high-quality synthetic and real-world training data. • Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. • Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. • Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. • Make pragmatic trade-offs and ship improvements quickly, learning from real usage. • Work under real production constraints: latency, cost, reliability, and safety

• Own the application engineering strategy and execution across backend, mobile, and desktop. • Lead and grow a small, senior applications team, including backend, mobile, and desktop engineers. • Ensure production-grade reliability: observability, monitoring, retries, fallbacks, and failure handling. • Set the architectural direction for AI-powered product workflows, APIs, and client integrations. • Ensure AI capabilities are integrated into the product with clear abstractions, predictable behavior, and graceful failure modes. • Partner closely with Machine Learning (ML) leadership to translate model capability into shippable product features. • Make high-impact decisions across latency, cost, reliability, security, and user experience. • Establish a strong execution culture through code reviews, design reviews, and hands-on technical leadership. • Ensure production readiness: observability, monitoring, retries, fallbacks, privacy, and cost controls. • Balance speed and discipline, shipping quickly without compromising long-term system quality.

• Technically oversee the architecture to identify, describe, and enrich events in historical vehicle logs using Multimodal LLMs. • Oversee the off-board ingestion and fusion of semantic scene descriptions, ego-centric kinematics, and internal autonomy telemetry to create a holistic diagnostic context for LLM inference. • Develop structured prompting templates utilizing Contextual Prompting (CP), Chain-of-Thought (CoT), and In-Context Learning (ICL) to evaluate scenarios. • Architect the integration of foundation models into the Metrics Engine (ME), designing efficient cascade filtering and log slice parallelization strategies to scale high-volume LLM inference across simulation and on-road drive logs while managing computational latency and costs. • Define, design, and implement key metrics to evaluate autonomous vehicle performance, such as lane change capability, oscillations, and braking. • Deploy and manage a Retrieval-Augmented Generation (RAG) vector database containing codified AV Driving Policies to ground off-board LLM evaluations in specific Operational Design Domains. • Serve as a technical escalation point and collaborate with Autonomy (Planner, Prediction, Perception) and Systems teams to deliver high-signal, enriched events. • Drive the transition toward Direct Vector-LLM Fusion utilizing emerging Physical AI ecosystems and open-weights Vision-Language-Action (VLA) models to process telemetry off-board without text-translation bottlenecks.

• Design, develop, and maintain end-to-end web applications using modern frontend and backend technologies. • Build responsive, high-performance user interfaces using React JS and related JavaScript or TypeScript frameworks. • Develop scalable backend services, RESTful APIs, and microservices using Node.js, Django, FastAPI, Flask, or similar frameworks. • Own full product development lifecycle activities including requirements analysis, architecture design, implementation, testing, deployment, monitoring, and continuous improvement. • Integrate AI capabilities into enterprise applications using LLMs, RAG pipelines, chatbot frameworks, and prompt engineering techniques. • Design and implement AI-enabled workflows including document ingestion, embeddings, vector search, retrieval optimization, and response generation. • Collaborate with product managers, UX designers, AI/ML engineers, DevOps teams, and business stakeholders to deliver reliable and user-focused solutions. • Ensure application security, scalability, performance, maintainability, and reliability across frontend, backend, database, and AI components. • Deploy, manage, and optimize applications and AI services on cloud platforms such as AWS, Google Cloud Platform (GCP), and Microsoft Azure. • Write clean, modular, well-tested, and maintainable code following software engineering best practices. • Mentor junior engineers, participate in code reviews, and contribute to technical design discussions and architecture decisions.