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Senior / Staff Software Engineer, ML-based Controls

Offre en anglais
  • Toronto, ON
  • Hybride
  • Publié 20 sept. 2026
  • 1 poste

241 000 $ US–320 000 $ US / année

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Type d’emploi
Temps plein
Niveau d’expérience
Expérimenté · 5+ ans
Formation minimale
Baccalauréat
Langue de l’offre
anglais
Heures de travail
40 heures par semaine

Résumé du poste

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.

Détails du poste

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.

Ce que vous ferez

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.

Exigences

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++.

Avantages

• Equity incentive awards • Annual performance bonus • Competitive perks

Compétences indiquées

  • Apprentissage automatique · Souhaitée
  • C++ · Souhaitée
  • Python · Souhaitée

Autres compétences pertinentes

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

  • 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

Domaines d’emploi

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

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