Senior / Staff Software Engineer, ML-based Controls

ProducePay; United States; On-site; USD 241,000 - 320,000; Full time

Benefits offered by this job

Equity incentives

Annual performance bonus

Competitive perks & benefits

Job summary

Waabi seeks engineers to design data-driven ML approaches for vehicle control and to develop learned models integrated into closed-loop simulations.

You will apply ML to adapt controllers across varying vehicles and conditions, work with a multidisciplinary team, and own projects from concept to on-vehicle validation with robust data tooling.

Qualifications

• MS/PhD or BS with 4+ years of industry experience in robotics/control/CS or related field.
• Strong foundation in control theory and dynamic systems (MPC, state estimation, system identification).
• Hands-on ML with physical systems and hardware-in-the-loop.
• Proficient in Python and C++, with PyTorch experience.
• Solid skills in linear algebra, optimization, statistics and probability.
• Ability to rapidly prototype and design experiments to verify results.
• Collaborative, open-minded team player passionate about self-driving tech.

Responsibilities

• Design and develop data-driven ML approaches for vehicle control.
• Develop learned models of vehicle behavior and integrate into closed-loop simulation.
• Apply ML to improve controller adaptation across vehicles and conditions.
• Collaborate with multidisciplinary engineers and scientists on safety-critical stack.
• Own problems end to end from concept to on-vehicle validation.
• Build data pipelines, evaluation metrics and tooling to measure learned vs baseline.
• Participate in architecture discussions on learning vs classical control.

Skills

Python

C++

Machine learning

PyTorch

Control theory

Linear algebra

Optimization

Statistics

Prototyping

Team collaboration

Education

MS/PhD or BS+4y

Job description

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 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.