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Job summary

Design and develop data-driven machine learning approaches for vehicle control and integrate them into closed-loop simulations. Own the end-to-end process from conceptualization and offline experimentation to on-vehicle validation and performance evaluation.

Job details

You Will… Design and develop data-driven and machine-learned approaches to vehicle control problems, bringing modern ML to a domain traditionally solved with classical methods. Develop learned models of vehicle behavior and dynamics, and integrate them into the closed-loop simulation. Apply machine learning to improve how the controller adapts across vehicles and operating conditions. Be part of a team of multidisciplinary Engineers and Research Scientists using an AI-first approach to enable safe self-driving at scale. Own problems end to end, from conceptualization and offline experimentation through simulation and on-vehicle validation. Build the data pipelines, evaluation metrics, and tooling needed to measure whether a learned approach outperforms the classical baseline. Participate and share ideas in technical and architecture discussions, helping define how learning and classical control coexist in a safety-critical stack. Qualifications: MS/PhD or Bachelors 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 (e.g., MPC, optimal control, state estimation, system identification, kinematic and dynamic vehicle modeling). Hands-on experience applying machine learning to a physical system, with real hardware in the loop rather than simulation alone. Production-quality coding skill in Python and C++, and experience with deep learning frameworks such as PyTorch. Solid problem solving skills using linear algebra, optimization, statistics & probability. Ability to rapidly prototype and test new algorithms, and to design the experiments that prove whether they work. Open-minded and collaborative team player with the willingness to help others. Passionate about self-driving technologies, solving hard problems, and creating innovative solutions. \n \n The US yearly salary range for this role is: $241,000 - $320,000 USD in addition to competitive perks & benefits. Waabi US Inc.’s yearly salary ranges are determined based on several factors in accordance with the Company’s compensation practices. Note: The Company provides additional compensation for employees in this role, including equity incentive awards and an annual performance bonus.

What you’ll do

Design and develop data-driven machine learning approaches for vehicle control and integrate them into closed-loop simulations. Own the end-to-end process from conceptualization and offline experimentation to on-vehicle validation and performance evaluation.

Requirements

Requires a Bachelor's, MS, or PhD in a technical field with at least 4 years of industry experience in robotics or controls. Candidates must demonstrate proficiency in control theory, dynamic systems, and production-quality coding in Python and C++.

Benefits

• Equity incentive awards • Annual performance bonus • Competitive perks

Listed skills

• Machine learning · Preferred
• C++ · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Machine learning
• Control theory
• Python
• C++
• PyTorch
• MPC
• Optimal control
• State estimation
• System identification
• Vehicle modeling
• Data pipelines
• Simulation
• Linear algebra
• Optimization
• Statistics
• Probability
• Problem Solving
• Tooling
• Electrical Engineering
• Innovation
• Algorithms
• Machine Learning
• Data Pipelines
• Artificial Intelligence
• Research
• Python (Programming Language)
• Computer Science
• Performance Metric
• Robotics
• Mechanical Engineering
• Simulations
• PyTorch (Machine Learning Library)
• C++ (Programming Language)
• Applied Machine Learning
• Open Mindset
• Deep Learning
• Linear Algebra
• Optimal Control
• Control Theories
• Dynamical Systems
• System Identification

Job areas

• Software
• Engineering
• Technology
• Data & Analytics
• Science & Research
• Staff Software Engineer
• Machine Learning Engineer
• Software Developers
• Computer and Information Research Scientists