Job Description
At Gatik, we connect people of extraordinary talent and experience to an opportunity to create a more resilient supply chain and contribute to our environment’s sustainability.
Key Responsibilities
- Gatik is revolutionizing middle-mile logistics with fully driverless commercial box trucks and tractors operating on fixed B2B routes. As a Senior Systems & Safety Engineer for AI/ML, you will bridge the gap between cutting-edge deep learning algorithms (perception, prediction, planning) and rigorous safety engineering. You will define the functional safety and SOTIF strategies for learned components, establish out-of-distribution (OOD) detection criteria, design runtime AI safety monitors, and construct evidence-backed safety cases for machine learning models running on Level 4 autonomous commercial vehicles.
- This role is onsite 5 days a week at our Santa Clara, CA office!
- SOTIF & ML Safety Architecture: Lead the implementation of ISO 21448 (SOTIF) and UL 4600 frameworks across Gatik's AI/ML pipelines.
- Safety Requirements for Learned Systems: Derive safety requirements and performance boundaries (e.g., false positive/negative limits, edge-case coverage metrics) for neural networks and deep learning models.
- Runtime Verification & AI Monitors: Design and validate deterministic AI safety guardrails, out-of-distribution (OOD) detectors, and runtime monitoring mechanisms that fall back to fail-safe actions upon model degradation or uncertainty.
- Dataset Safety & Bias Assessment: Establish criteria for training and validation dataset completeness, operational design domain (ODD) coverage, class imbalance, and synthetic vs. real data fidelity.
- Failure Modes & Effects Analysis: Conduct systematic HAZOP, STPA, and SOTIF risk analyses specifically targeting deep learning failure modes (e.g., adversarial examples, sensor degradation, lighting/weather edge cases).
- Safety Case Argumentation: Author structured safety case arguments (Goulburn/GSN or Goal Structuring Notation) demonstrating acceptable residual risk for AI components in driverless operations.
Requirements
- Education: M.S. or Ph.D. in Computer Science, Robotics, Electrical Engineering, Machine Learning, or related field.
- Experience: 6+ years in systems/safety engineering or autonomous systems, with at least 3 years explicitly focused on AI/ML safety.
- Standards Expertise: Deep working knowledge of ISO 21448 (SOTIF), ISO/PAS 8800, UL 4600, and ISO 26262
- Technical Skills:
- AV Domain Knowledge: Strong understanding of sensor modalities (LiDAR, Radar, Cameras, IMUs) and their failure modes in adverse weather/environmental conditions.
- Track record of taking a machine-learning-based L4 autonomous vehicle system to driverless commercial deployment.
- Direct experience with data generation/curation tools, active learning, and automated edge-case extraction pipelines.
- Salary Range - $160,000- $220,000
Ready to Apply?
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