Senior / Staff Software Engineer, ML-based Controls

Waabi; Kentucky; On-site; USD 241,000 - 320,000; Full time

Benefits offered by this job

Equity incentives

Annual performance bonus

Competitive benefits

Job summary

Waabi in the United States is seeking a Senior ML/Robotics Engineer to design data-driven control approaches for autonomous driving. You will develop learned models and integrate them into closed-loop simulation to advance safe self-driving at scale.

The role requires deep expertise in control theory and ML with hands-on deployment on hardware-in-the-loop, and you will work with a multidisciplinary AI-first team on end-to-end ownership, experimentation, and robust evaluation.

Qualifications

• MS/PhD or Bachelor's with 4+ years industry experience in Robotics, Controls, Mechanical/Electrical Eng, CS or similar field.
• 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.
• 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 experiments that prove their effectiveness.
• Open-minded and collaborative team player with willingness to help others.

Responsibilities

• 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.

Skills

control theory & dynamic systems

machine learning for physical systems

Python & C++ coding

linear algebra & optimization

rapid prototyping

team collaboration

self-driving passion

Education

MS/PhD or Bachelor's with 4+ years of industry experience

Tools

PyTorch

Job description

• 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.

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.

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.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.