Motional

Motional

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

Pittsburgh, Pennsylvania, United States · Principal

Sponsorship not specified$200k-$275kDetected 7 days ago
PythonC++Vector DatabasesAWSGCPCloud PlatformsMachine LearningData EngineeringNLPLLMsRAGRoboticsLeadershipCollaborationProblem Solving

About the role

  • Backed by Hyundai Motor Group, Motional is at the forefront of the physical AI revolution.
  • The Systems Readiness and Performance team is the crucial bridge between software development and real-world deployment.
  • While this role will pioneer future off-board Vision-Language-Action (VLA) integrations for off-board analysis, it requires strong foundations as an Autonomous Vehicle Performance Metrics developer.

Responsibilities

  • 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.
  • 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.
  • 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.
  • Serve as a technical escalation point and collaborate with Autonomy (Planner, Prediction, Perception) and Systems teams to deliver high-signal, enriched events.
  • We aren't just developing driverless cars
  • To do so successfully, we must design for everyone in our cities and on our roads.

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.
  • 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.
  • Expert-level proficiency in Python and strong understanding of software development principles.

Nice to have

  • 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.

Skills

  • Technically oversee the architecture to identify, describe, and enrich events in historical vehicle logs using Multimodal LLMs.

Compensation

  • $200,000 - $275,000 USD

Benefits

  • Prompt Engineering & Model Integration: Develop structured prompting templates utilizing Contextual Prompting (CP), Chain-of-Thought (CoT), and In-Context Learning (ICL) to evaluate scenarios.

Equal opportunity

  • Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.
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This listing is sourced directly from Motional's careers page and normalized into a canonical job model.