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Kodiak · 1 month ago

Quantitative Risk Estimation Engineer

San Francisco Bay Area

Full-time

Onsite

Senior Level

$200K/yr - $268K/yr

5+ years exp

Kodiak Robotics, Inc. is a leader in autonomous ground transportation, committed to a safer and more efficient future. The Quantitative Risk Estimation Engineer will focus on developing and implementing risk estimation models and safety strategies for autonomous vehicles, working closely with a high-impact team to ensure safety in autonomous driving systems.

Autonomous VehiclesLogisticsRoboticsSoftwareTransportation

Growth Opportunities

No H1B

U.S. Citizen Only

Responsibilities

Write Python code to estimate risk using Bayesian probabilistic models in our Probabilistic Risk Assessment

Write C++ code to efficiently search for failure modes in insanely high-dimensional search spaces

Run simulations and perform analysis to generate risk-informed results that shape engineering priorities at the company

Pioneer new methods for practically achieving incredibly high safety levels in autonomous vehicles

Perform hazard analysis, risk assessments, and fault tree analysis

Collaborate cross-functionally to integrate safety throughout development

Develop and execute comprehensive validation strategies

Drive continuous improvement in safety processes and tools

Qualification

Bayesian probabilistic modelsSensor FusionMachine Learning AlgorithmsControl algorithmsPythonC++Systems Engineering V-ModelSafety analysisContinuous improvementCollaborationProblem-solving

Required

Strong grasp of kinematics, dynamics, and modeling

Proficiency in Python, Matlab, C++, or C

Strong experience (you should have implemented or be able to explain) in multiple of these domains: Sensor Fusion and Object Tracking (e.g. Kalman filtering, particle filters, least squares estimation), Supervised Machine Learning Algorithms (Neural Networks, SVMs, kNN, linear regression, logistic regression, decision trees, etc.), Classical Computer Vision (e.g. edge detection, corner detection, extrinsic calibration, intrinsic calibration, optics), Motion Planning Algorithms, (e.g. A*, potential fields, RRT), Control algorithms (e.g. PID, non-linear phase-plane controllers, model predictive control)

Strong grasp of probability and statistics

Familiarity with autonomy systems architectures

Experienced in Systems Engineering V-Model

Familiar with SOTIF & UL4600

Experience with any of ISO 26262, DO 178, IEC 61508 standards, particularly on the highest level of criticality (e.g. ASIL-D, DAL A, SIL 4)

Experience performing safety analysis, such Fault Trees Analysis (FTA), Failure Mode Effects Analysis (FMEA), Hazard Analysis and Risk Assessment (HARA)