Senior Staff Software Engineer, ML-based Controls

Waabi

. Design and develop data-driven and machine-learned approaches to vehicle control problems .

Posted 9/18/2026full-timeArizona • United StatesSenior💰 $241,000 - $320,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in machine learning applications for vehicle control, with a strong foundation in control theory, dynamic systems, and hands-on experience in real hardware integration. Proficient in Python and C++, with the ability to collaborate effectively in multidisciplinary teams to advance self-driving technology.

Highest-signal resume keywords

Machine Learning ApplicationControl Theory ExpertisePython ProgrammingC++ ProgrammingDeep Learning Frameworks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Control TheoryDynamic SystemsModel Predictive Control (MPC)State EstimationSystem IdentificationKinematic Vehicle ModelingMachine LearningAlgorithm PrototypingLinear AlgebraOptimization

Soft Skills

Problem-SolvingCollaborationCommunication

Tools & Technologies

PyTorchData PipelinesSimulation ToolsEvaluation Metrics

Industry Keywords

RoboticsSelf-Driving TechnologySafety-Critical SystemsVehicle Dynamics

Tech Stack

Tools & technologies

PythonPyTorchC++

About the role

Key responsibilities & impact

Design and develop data-driven and machine-learned approaches to vehicle control problems
Develop learned models of vehicle behavior and dynamics and integrate them into closed-loop simulation
Apply machine learning to improve controller adaptation across vehicles and operating conditions
Collaborate with multidisciplinary Engineers and Research Scientists using an AI-first approach for safe self-driving at scale
Own problems end to end, from conceptualization and offline experimentation through simulation and on-vehicle validation
Build data pipelines, evaluation metrics, and tooling to compare learned approaches with classical baselines
Participate in technical and architecture discussions and help define how learning and classical control coexist in a safety-critical stack

Requirements

What you’ll need

MS/PhD or Bachelor's degree with a minimum of 4 years of industry experience in Robotics, Controls, Mechanical/Electrical Engineering, Computer Science and/or similar technical field(s) of study
Demonstrated depth in control theory and dynamic systems, including MPC, optimal control, state estimation, system identification, and kinematic and dynamic vehicle modeling
Hands-on experience applying machine learning to a physical system with real hardware in the loop
Production-quality coding skills in Python and C++
Experience with deep learning frameworks such as PyTorch
Problem-solving skills using linear algebra, optimization, statistics, and probability
Ability to rapidly prototype and test new algorithms and design experiments to validate them
Ability to work collaboratively and help others
Passion for self-driving technologies and solving complex problems

Benefits

Comp & perks

Competitive perks & benefits
Equity incentive awards
Annual performance bonus