Principal Engineer Tech Lead, Embodied AI & Off-Board Performance Evaluation
New
M
MotionalAutonomous Vehicles
Remote U.S.Full-TimePrincipal
Salary$200,000 — $275,000 USD
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Job Details
- Experience
- 10+ years of professional experience in software engineering, applied AI/ML, or autonomous vehicle systems development.
- Required Skills
- PythonMachine Learning
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.
- 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.
- Expert-level proficiency in Python and strong understanding of software development principles.
Responsibilities
- 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.
- Develop structured prompting templates utilizing Contextual Prompting (CP), Chain-of-Thought (CoT), and In-Context Learning (ICL).
- Architect the integration of foundation models into the Metrics Engine (ME), designing efficient cascade filtering and log slice parallelization.
- 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.
- Serve as a technical escalation point and collaborate with Autonomy (Planner, Prediction, Perception) and Systems teams.
- Drive the transition toward Direct Vector-LLM Fusion utilizing emerging Physical AI ecosystems and VLA models.
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