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

• 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.

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.

Core Competencies

Demonstrates expertise in architecting and integrating Large Language Models (LLMs) and Vision-Language Models (VLMs) for autonomous vehicle systems, with a strong focus on performance metrics evaluation and efficient data processing. Proficient in developing frameworks and tools for complex system analysis and operational design domain applications.

Highest-signal resume keywords

• Large Language Models (LLMs)
• Vision-Language Models (VLMs)
• Python Programming
• Autonomous Vehicle Systems Development
• Cloud Vector Databases

ATS Optimization Keywords

Hard Skills

• Software Engineering
• Applied AI/ML
• Parameter-Efficient Fine-Tuning
• Adversarial Scenario Generation
• Performance Metrics Calculation

Soft Skills

• Analytical Skills
• Problem-Solving Skills

Industry Keywords

• Autonomous Vehicle Performance
• Semantic Scene Descriptions
• Ego-Centric Kinematics
• Operational Design Domains
• Direct Vector-LLM Fusion

Tools & Technologies

• LanceDB
• Retrieval-Augmented Generation (RAG)
• Metrics Engine (ME)