Technical Lead- Automotive AI Cockpit

Bosch Group•Plymouth Charter Township, MI

About The Position

We are seeking a highly skilled Technical Lead / Architect to drive the design, development, and delivery of next-generation AI-powered smart cockpit solutions for Automotive. This role will lead the technical vision and execution of an intelligent, context-aware in-vehicle experience that transforms the cockpit into a proactive, personalized companion for drivers and passengers. You will work at the intersection of AI, embedded systems, and automotive software, shaping scalable architectures while coordinating cross-functional teams to bring innovative cockpit experiences to production.

Requirements

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field with 5+ years of experience in AI/ML-driven system development.
Strong hands-on expertise in machine learning, deep learning, and AI system design, with experience deploying models on edge, embedded, or automotive platforms.
Experience integrating AI and Generative AI/LLM-based capabilities into real-time, resource-constrained environments.
Strong knowledge of edge AI optimization techniques (quantization, pruning, distillation) and proficiency in Python and C++, along with experience in embedded AI lifecycle management, including OTA updates and fleet telemetry.
Hands-on experience with LLM/generative-AI orchestration and model fine-tuning/adaptation in real-time, resource-constrained environments.
Strong understanding of cloud platforms architecture and edge–cloud orchestration, and proven ability to lead cross-functional engineering teams.

Nice To Haves

Knowledge of automotive cockpit systems (voice assistants & personalization)
Experience with on-device voice pipelines (ASR/TTS) and inference-serving stacks on various SoC/processors
Exposure to user experience design for in-vehicle systems

Responsibilities

Define and own the end-to-end architecture for AI-driven smart cockpit systems across hardware, middleware, and application layers
Lead design and implementation of AI/ML pipelines, model optimization, deployment, and lifecycle management
Architect multi-model/LLM orchestration and on-device model adaptation (fine-tuning, distillation) for edge automotive deployment, including model selection, routing, and inference optimization on GPU/NPU SoCs
Architect efficient edge AI execution, optimizing models for CPU/GPU/NPU (quantization, pruning, latency, power)
Design hybrid edge–cloud architectures for personalization, continuous learning, and scalable feature delivery
Integrate multimodal AI capabilities (voice, vision, sensor fusion) with real-time and safety-critical constraints
Establish robust data pipelines, telemetry, and feedback loops, enabling continuous model validation, performance monitoring, and iterative improvement of AI capabilities in production
Define the AI evaluation and observability framework, including model quality testing, regression checks, and production monitoring tied to release readiness
Drive technical program execution across architecture, AI, and integration workstreams — including planning, dependencies, risk management, milestone tracking, cross-functional coordination, and stakeholder communication — to ensure on-time, high-quality production readiness