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Salary: USD 149,000 - 188,000 / annual
Atoms is building Physical AI—real-world robots for industries including food, mining, and transport. The company integrates hardware, software, AI, operations, manufacturing, and real estate to deploy autonomous systems at scale.
As a Safety Case Engineer, you will own a defined scope of the safety case end-to-end, from initial draft through sign-off and ongoing maintenance. Your responsibilities include:
- Author and defend safety arguments, defining scope, assumptions, exclusions, and hazards that cannot arise within your defined boundaries.
- Judge evidence sufficiency by assessing operating experience, coverage against each case, and evidence provenance in close collaboration with functional safety, scenario validation, and fleet operations leads.
- Write claim-argument-evidence maps that clearly show when arguments hold and when they do not.
- Convene reviews when work is ready, carry reviewer comments to documented resolution, and serve as independent reviewer on scopes you do not own.
- Act as a demanding customer of shared methods and tooling; escalate missing rules, ambiguities, or tools that make the wrong thing easy rather than implementing local workarounds.
- Use AI tooling to support the work: drafting from templates, generating and stress-testing hazard sets, cross-checking coverage against scenario libraries, tracing hazards to tests to evidence, and identifying where upstream revisions have invalidated prior work.
This role requires exceptional written precision—your deliverables must stand up without you in the room. You will work in a collaborative environment with three interchangeable seats, requiring focus on your own scope, flexibility to support colleagues, and genuine peer review contributions.
REQUIREMENTS:
- 7+ years across functional safety, autonomy behavior, and fleet operations, with hands-on depth in at least two domains and working fluency in the third.
- Demonstrated authorship of a structured safety argument (GSN or equivalent claim-argument-evidence structure) on a real system, defended to critical review. Be prepared to discuss one you wrote and what you would change.
- Demonstrated ownership of hazard analysis (HARA, FMEA, FTA, or equivalent) AND scenario-based validation ownership—both required, not either alone.
- Rigorous approach to provenance: instinctively distinguish measurements from estimates from placeholders and label them accordingly.
- Willingness to speak up about readiness and safety concerns, even when commercially inconvenient.
- Standards fluency across ISO 26262, ISO 21448 (SOTIF), UL 4600, and SAE J3016, with judgment to apply them proportionately to scoped arguments.
- Comfort with statistics of exposure, rare events, coverage, and surrogate safety measures; understanding their limits (e.g., kinematic proximity metrics measure proximity dynamics, not harm).
- Programming ability in Python or similar, sufficient to work with fleet data, build tooling, and work fluently with AI coding assistants—with clear boundaries: generated hazard lists are hypotheses to verify; generated argument maps are drafts, never signed arguments.
- Exceptional written precision; vague writing is a safety problem, not a style issue.
