Staff Specialist Field Engineer, Autonomous Vehicles
$161,000–$215,000 year
On-siteSan Francisco, California, United States
Job Summary
Define the technical standard for the Physical AI Field Engineering practice and lead complex customer engagements from first contact through embedded deployment in autonomous vehicle verticals. Build and iterate on customer-facing applications using the CoreWeave product stack to design solutions tailored to specific engineering use cases, while gathering signals from real deployments to inform standalone product capabilities. Validate ML solutions independently using Python and modern frameworks, ensuring results align with physical and safety constraints of the domain. Translate field observations into structured product signals and contribute to the wider team's knowledge framework for L4-5 autonomous systems.
Required Qualifications
- 8+ years experience in autonomous vehicle systems development, perception engineering, motion planning, or AI/ML for L4-5 autonomous systems
- Deep familiarity with autonomous vehicle sensor modalities (camera, LiDAR, radar) and their data characteristics including the long-tail distribution and edge case challenges central to L4-5 development
- Hands-on ML capability: able to build, validate, and deploy ML solutions independently in Python using modern ML frameworks
- Proficient in working directly in JupyterHub, VS Code, Marimo, and W&B Models to build and deploy customer-facing applications
- Working knowledge of ML approaches relevant to autonomous systems: perception, object detection, motion prediction, closed-loop simulation, or anomaly detection in autonomous system logs
- Able to validate ML solutions on engineering grounds and identify when a result is inconsistent with the physical or safety constraints of the domain
- Proven experience leading complex technical customer engagements, managing multi-stakeholder environments, and maintaining executive relationships
- Strong understanding of the autonomous vehicle development pipeline, including closed-loop simulation, scenario-based testing, and data driven approaches to safety case validation
- Able to translate field observations into structured product signals that are actionable for an engineering team
- Bachelor's or Master's degree in Electrical Engineering, Systems Engineering, Computer Science, Physics, or a related technical discipline
Desired Qualifications
- Knowledge of functional safety standards including ISO 26262 and SOTIF
- Experience with simulation environments used in autonomous vehicle development (CARLA, LGSVL or similar)
- Familiarity with edge deployment of ML models in constrained or safety-critical environments
- Experience identifying and progressing expansion opportunities within strategic customer accounts
- Track record of contributing to internal knowledge frameworks, technical publications, or industry forums in the autonomous vehicles space
- Experience working alongside or within an AI infrastructure or MLOps environment
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