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Senior / Staff Software Engineer, ML-based Controls

  • Toronto, ON, Canada

$241,000 to $320,000 USD / year · Full-time

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Job details

Pay
$241,000 to $320,000 USD / year
Job type
Full-time
Posting language
English
Posted

Original 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.

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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.

More about this job

Responsibilities

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++.

Skills

  • Machine learning
  • Control theory
  • Python
  • C++
  • PyTorch
  • MPC
  • Optimal control
  • State estimation
  • System identification
  • Vehicle modeling
  • Data pipelines
  • Simulation
  • Linear algebra
  • Optimization
  • Statistics
  • Probability

Benefits

  • Equity incentive awards
  • Annual performance bonus
  • Competitive perks

Visa sponsorship

Not detected in the job text

Categories

  • Software
  • Engineering
  • Technology
  • Data & Analytics
  • Science & Research

Keywords

  • Machine learning
  • Control theory
  • MPC
  • Optimal control
  • State estimation
  • System identification
  • Vehicle dynamics
  • Python
  • C++
  • PyTorch
  • Simulation
  • Data pipelines
  • Linear algebra
  • Optimization
  • Statistics
  • Probability
  • Robotics
  • Self-driving
  • AI
  • Hardware in the loop
  • Algorithm development
  • Software engineering
  • Autonomy
  • Algorithms

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