Functional Safety Lead

Atoms•San Francisco, CA

1d•$182,000 - $238,000•Onsite

About The Position

This role owns the technical safety analysis of Atoms' automated vehicle platform and sensor suite. The Functional Safety Lead will lead the fault and failure analysis of the platform and its sensing chain, determining how failures manifest, how the system behaves, and whether that behavior is satisfactory. This role also determines the safety impact of changes, such as hardware revisions, sensor changes, mounting and calibration updates, software releases, new platform variants, and expansions of the operating domain. This is a technical individual contributor role with significant impact, where the lead will own the analysis and documentation, and their assessment will carry weight in development decisions.

Requirements

8+ years in functional safety for automotive, automated vehicles, aerospace, industrial automation, or another safety-critical hardware domain, with deep hands-on analysis experience.
Demonstrated ownership of HARA, FMEA, and FTA on real systems that shipped.
Strong standards fluency: ISO 26262, ISO 21448 (SOTIF), and UL 4600.
Working knowledge of ISO/SAE 21434 for cybersecurity, ISO 34502 or equivalent scenario-based evaluation approaches, and SAE J3016.
Real hardware and electrical depth: sensors, actuation interfaces, power and grounding, wiring, EMC, redundancy architecture.
Comfortable working with schematics and oscilloscopes at the vehicle level.
Programming ability (Python or similar) sufficient to analyze field data, build tooling, and work with an AI coding assistant.
Ability to use LLM-based and purpose-built safety-analysis tooling as a working instrument.
Ability to build their own tooling rather than filing tickets for it.
Understanding of where AI assistance is legitimate and where it is not, with rigor about the boundary between AI-generated hypotheses and human-justified safety claims.
Curiosity and a fast pace in developing AI tooling.

Nice To Haves

Experience integrating safety-critical systems onto vehicle platforms designed by another organization.
Experience authoring or defending a safety case to an external party.
Certification such as Functional Safety Engineer, or equivalent demonstrated depth.
Experience with fault injection frameworks, HIL, or automated safety verification pipelines.

Responsibilities

Own HARA for the platform, derive safety goals and requirements, and drive them into the architecture. Maintain traceability from hazard to requirement to verification evidence.
Lead FMEA across the platform (actuation interfaces, redundancy, and fallback paths) and across the sensor suite (sensor faults, degraded and partially obscured modes, and silent failure modes).
Own the analysis of sensing sufficiency and degradation, including coverage, erosion under adverse conditions, tolerable sensor loss combinations, fault detection speed, and system response.
Own the process and judgment for change impact analysis, defining triggers, depth of assessment, evidence requirements, and escalation procedures to ensure timely safety assessments.
Build and maintain the safety argument for the platform, structured against ISO 26262, ISO 21448 (SOTIF), and UL 4600, working with safety case leads on standard interpretation.
Define evidence required to close each safety claim (fault injection, HIL and vehicle-level testing, field data, or analysis) and partner with engineering to build necessary fault injection and degraded-mode test capabilities.
Work with safety operations so that field events, disengagements, and investigation findings flow into hazard analysis, and identified hazards produce operational controls where engineering controls are not yet available.
Utilize AI systems purpose-built to accelerate safety analysis, including hazard identification, failure mode generation, traceability, and documentation.
Write code and scripts to automate tasks such as pulling field data, checking traceability, syncing analysis artifacts with design, and identifying stale safety case elements.
Reason about the safety implications of ML-based components within the system, specifically for perception or planning stacks.