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Salary: USD 176,000 - 242,000 / 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 into real environments at scale.
You will own diagnostics and failure analysis across the development fleet. Your core responsibility is taking failures from logs to assigned root causes, owning triage classification, severity modeling, and the diagnostic tooling that determines how quickly the team identifies what failed. You will build diagnostic coverage and automated classification systems that transform failure analysis from a manual engineering activity into a scalable process where only novel failures require human investigation.
Key responsibilities:
- Triage failures as software, firmware, hardware, data path, or system-level regressions, routing them with evidence
- Investigate novel failures using logs, telemetry, bus traces, and video to establish root cause
- Build parsing, correlation, and visualization tools that make large log datasets tractable for engineers
- Develop frameworks that detect known failure signatures automatically, catching recurrence via software
- Own monitoring for the logging path itself: dropped frames, timestamp drift, bandwidth and storage limits, and alerting that catches data quality issues the same day
- Categorize failure severity consistently and identify failure clusters that should set engineering priority
- Author troubleshooting guides and set standards for complete root cause analysis documentation
The role spans multiple vehicle platforms with different hardware, compute, and software stacks, and requires regular hands-on time in the garage and around vehicles. You will work onsite five days a week in San Francisco with 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 diagnosing failures spanning hardware and software with specific examples of root cause establishment
- Ability to correlate hardware symptoms and video evidence with telemetry and bus data into concrete timelines
- Sensor-level troubleshooting depth on cameras, lidar, radar, GNSS, and IMU, including timing failure modes
- Strong working 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
- Experience with monitoring and observability platforms and log management at fleet scale
- 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 a week in San Francisco with periodic travel to test sites
