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Kodiak

Probabilistic Risk Assessment Engineer

San Francisco Bay AreaFrom $210kmidAdded yesterday

About this role

Kodiak Robotics seeks a Probabilistic Risk Assessment Engineer to develop safety assurance methods for autonomous trucks, targeting exceptional safety levels through Bayesian modeling, failure mode analysis, and innovative validation strategies. You'll blend Python and C++ development with systems thinking to pioneer new approaches to proving autonomous vehicle safety.

What you'll do

  • Develop Python-based Bayesian probabilistic models to estimate and quantify risk in autonomous systems
  • Write C++ code to efficiently search high-dimensional spaces for potential failure modes
  • Conduct hazard analysis, fault tree analysis, and comprehensive risk assessments
  • Execute simulations and generate risk-informed analyses to guide engineering priorities
  • Design and lead validation and testing strategies for safety case development
  • Collaborate across teams to embed safety practices throughout the product development lifecycle

What they're looking for

  • Python programming (Bayesian modeling, probabilistic methods)
  • C++ for performance-critical applications
  • Probabilistic risk assessment and statistical analysis
  • Hazard analysis and fault tree analysis (FMEA, FTA)
  • Autonomous systems and vehicle safety
  • Systems thinking and first-principles problem-solving
  • Simulation and modeling techniques
  • Technical documentation and safety case development
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Kodiak

Kodiak builds autonomous trucking systems and software that enable self-driving vehicles for commercial transportation. The company is hiring engineers across verification & validation, vehicle integration, behavior planning, sensor hardware, and manufacturing quality to develop and deploy its autonomous driving technology.

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Likely interview questions

  • Describe your experience applying Bayesian probabilistic models to real-world safety-critical systems—what were the key challenges?
  • Walk us through how you'd approach searching a high-dimensional failure space for an autonomous system; what algorithms and tools would you use?
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