Principal Engineer Tech Lead, Embodied AI & Off-Board Performance Evaluation

Motional is a leading autonomous driving company on a mission to make driverless vehicles a safe, reliable, and accessible reality. They are seeking a talented Principal Engineer to oversee the design, development, and deployment of an Embodied AI and Large Language Model (LLM)-based monitoring framework for off-board scenario understanding and intelligence evaluation. This role involves leading the creation of a specialized evaluation layer that analyzes autonomous vehicle performance metrics to enhance safety and operational efficiency.

Responsibilities:

• Embodied AI & LLM Framework Oversight: Technically oversee the architecture to identify, describe, and enrich events in historical vehicle logs using Multimodal LLMs
• Multi-Modal Data Fusion: 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
• Prompt Engineering & Model Integration: Develop structured prompting templates utilizing Contextual Prompting (CP), Chain-of-Thought (CoT), and In-Context Learning (ICL) to evaluate scenarios
• AI Inference & Evaluation Scalability: 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
• AV Performance Metrics Foundations: Define, design, and implement key metrics to evaluate autonomous vehicle performance, such as lane change capability, oscillations, and braking
• Driving Policy Integration: 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
• Cross-Functional Technical Leadership: Serve as a technical escalation point and collaborate with Autonomy (Planner, Prediction, Perception) and Systems teams to deliver high-signal, enriched events
• Advanced Physical AI R&D: 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

Requirements:

• 10+ years of professional experience in software engineering, applied AI/ML, or autonomous vehicle systems development
• Bachelor's degree in Computer Science, Engineering, Robotics, or a related field
• Proven experience working with Large Language Models (LLMs) and Vision-Language Models (VLMs) for reasoning, parsing, and scene description
• Experience with parameter-efficient fine-tuning and deploying open-weights models on internal infrastructure
• Familiarity with local and cloud vector databases, such as LanceDB, for housing output vector embeddings
• Experience with adversarial scenario generation and closed-loop simulation environments
• Strong background leveraging software to develop frameworks, libraries, and tools for calculating and aggregating AV performance metrics
• Strong analytical and problem-solving skills, particularly in the context of complex system performance evaluation
• Expert-level proficiency in Python and strong understanding of software development principles
• Experience working with autonomous vehicle sensor data, including its processing and integration
• Hands-on experience with data pipeline orchestration tools and distributed data processing frameworks
• Expertise managing cloud infrastructure on AWS or GCP for processing terabytes of data efficiently
• Familiarity with Ray and Ray clusters for scaling Python applications and AI/ML tasks
• Expertise with C++ programming for data frameworks