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Salary: USD 182,000 - 238,000 / annual
Atoms is building Physical AI—real-world robots for industries including food, mining, and transport. The company integrates hardware, software, AI, operations, and manufacturing to deploy autonomous systems at scale.
You will own how new vehicle platforms enter the fleet, taking platform variants, sensors, or compute generations from first article through fleet release. This role spans multiple vehicle platforms with different hardware, compute, and software architectures, requiring regular hands-on time in the garage and around vehicles.
Key responsibilities:
- Own first integration and bring-up for new platform variants, sensor generations, compute generations, and power or network architecture changes
- Convert one-off builds into repeatable commissioning procedures covering firmware, calibration, sign-offs, and autonomy software integration that technicians can execute
- Define and run vehicle-level validation checks that new platforms must pass before release
- Establish and document the relationship between bench test results and vehicle behavior for each platform
- Own multimodal calibration, time synchronization, and sensor-to-compute data integrity on new platforms
- Lead cross-functional debugging on failures spanning mechanical, electrical, firmware, network, and autonomy boundaries until root cause is established
- Write integration guides, troubleshooting trees, and go/no-go criteria for technician teams and engineers; enforce conventions for labeling, documentation, and configuration control
The role is based in San Francisco with five-day-per-week onsite requirement and periodic travel to test sites.
REQUIREMENTS:
- Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, or related field
- 6+ years in systems integration, diagnostics, or hardware-software interface engineering on vehicles
- L4 program or production ADAS experience
- Track record of diagnosing failures spanning hardware and software with documented root-cause analysis
- Ability to correlate hardware symptoms, video evidence, telemetry, and bus data into concrete timelines
- Sensor-level troubleshooting expertise: cameras, lidar, radar, GNSS, IMU, including timing failure modes
- Strong knowledge of vehicle networks and diagnostics: CAN, CAN FD, Automotive Ethernet, DTCs, and failure presentation at each layer
- Python and command-line log analysis at production-tool level
- Experience with automated triage or anomaly detection over fleet data
- Familiarity with monitoring, observability platforms, and fleet-scale log management
- Working experience with cloud storage and databases for telemetry: object storage, SQL, time-series, or NoSQL stores
- Experience with data collection or logging fleets, particularly diagnosing data-path problems at volume
- Ability to work onsite five days per week in San Francisco with periodic travel to test sites
