Senior Systems & Safety Engineer – AI/ML

Socket.dev; Santa Clara (CA); On-site; USD 160,000 - 220,000; Full time

Job summary

Gatik in Santa Clara, CA is seeking a Senior Systems & Safety Engineer for AI/ML to bridge deep learning and safety engineering. You will define functional safety and SOTIF strategies for learned components, establish OOD criteria, and build runtime AI safety monitors for Level 4 autonomous vehicles.

You will lead safety analyses (HAZOP, STPA, SOTIF), craft safety cases (GSN), and work with sensor domains (LiDAR, Radar, Cameras).

Qualifications

• MS/PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or related field.
• 6+ years in systems/safety engineering or autonomous systems, with at least 3 years explicitly focused on AI/ML safety.
• Deep working knowledge of ISO 21448 (SOTIF), ISO/PAS 8800, UL 4600, and ISO 26262

Responsibilities

• Lead the implementation of ISO 21448 (SOTIF) and UL 4600 frameworks across Gatik's AI/ML pipelines.
• Derive safety requirements and performance boundaries for neural networks and deep learning models.
• Design and validate deterministic AI safety guardrails, OOD detectors, and runtime monitoring mechanisms with fail-safe actions.
• Establish criteria for training/validation dataset completeness, ODD coverage, class imbalance, and synthetic vs. real data fidelity.
• Conduct HAZOP, STPA, and SOTIF risk analyses targeting deep learning failure modes.
• Author structured safety case arguments (GSN/Goal Structuring Notation) demonstrating residual risk is acceptable.

Skills

SOTIF safety

UL 4600

ISO 21448

ISO 26262

AV domain knowledge

Safety requirements

Education

MS/PhD in CS/Robotics

Job description

Who we are

Gatik, the leader in autonomous middle-mile logistics, is revolutionizing the B2B supply chain with its autonomous transportation-as-a-service (ATaaS) solution and prioritizing safe, consistent deliveries while streamlining freight movement by reducing congestion. The company focuses on short-haul, B2B logistics for Fortune 500 retailers and in 2021 launched the world’s first fully driverless commercial transportation service with Walmart. Gatik's Class 3-7 autonomous trucks are commercially deployed across major markets, including Texas, Arkansas, and Ontario, Canada, driving innovation in freight transportation.

The company's proprietary Level 4 autonomous technology, Gatik Carrier™, is custom-built to transport freight safely and efficiently between pick-up and drop-off locations on the middle mile. With robust capabilities in both highway and urban environments, Gatik Carrier™ serves as an all-encompassing solution that integrates advanced software and hardware powering the fleet, facilitating effortless integration into customers' logistics operations.

About the role

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!

What you'll do

• 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.

What we're looking for

• 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.

Preferred Qualifications

• 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

More about Gatik

Founded in 2017 by experts in autonomous vehicle technology, Gatik has rapidly expanded its presence to Mountain View, Dallas-Fort, Arkansas, and Toronto. As the first and only company to achieve fully driverless middle-mile commercial deliveries, Gatik holds a unique and defensible position in the AV industry, with a clear trajectory toward sustainable growth and profitability.

We have delivered complete, proprietary AV technology - an integration of software and hardware - to enable earlier successes for our clients in constrained Level 4 autonomy. By choosing the middle mile – with defined point-to-point delivery, we have simplified some of the more complex AV challenges, enabling us to achieve full autonomy ahead of competitors. Given extensive knowledge of Gatik’s well-defined, fixed route ODDs and hybrid architecture, we are able to hyper-optimize our models with exponentially less data, establish gate‑keeping mechanisms to maintain explainability, and ensure continued safety of the system for unmanned operations.

Visit us at Gatik for more company information and Careers at Gatik for more open roles.

Notable News

• Forbes: Forget robotaxis. Upstart Gatik sees middle-mile deliveries as the path to profitable AVs
• Tech Brew: Gatik AI exec unpacks the regulations that could shape the AV industry
• Business Wire: Gatik Paves the Way for Safe Driverless Operations (‘Freight‑Only’) at Scale with Industry‑First Third‑Party Safety Assessment Framework
• Auto Futures: Autonomous Trucking Group Gatik Secures Investment From NIPPON EXPRESS HOLDINGS
• Automotive News: Gatik foresees hundreds of self‑driving trucks on road soon, and that's just the beginning
• Forbes: Isuzu And Gatik Go All In To Scale Up Driverless Freight Services
• Bloomberg: Autonomous Vehicle Startup Takes Off by Picking Off Easier Routes
• Reuters: Driverless vehicles on limited routes bump along despite US robotaxi scrutiny

Taking care of our team

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. We are diverse in our backgrounds and perspectives yet united by a bold vision and shared commitment to our values. Our culture emphasizes the importance of collaboration, respect and agility.

We at Gatik strive to create a diverse and inclusive environment where everyone feels they have opportunities to succeed and grow because we know that together we can do great things. We are committed to an inclusive and diverse team. We do not discriminate based on race, color, ethnicity, ancestry, national origin, religion, sex, gender, gender identity, gender expression, sexual orientation, age, disability, veteran status, genetic information, marital status or any legally protected status.