Publié il y a 2 mois
Senior / Staff Machine Learning Engineer, Vehicle Dynamics
@ Waabi
Toronto, Ontario
En bref
Waabi is hiring a Senior or Staff Machine Learning Engineer for vehicle dynamics to bring learned models into vehicle control for self-driving. The work covers learned vehicle behaviour models, closed-loop simulation, and on-vehicle validation. The posting states a US salary range of $241,000 to $320,000 USD plus equity and an annual bonus.
Responsabilités :
- Design data-driven and machine-learned vehicle control approaches
- Develop learned models of vehicle behaviour and dynamics
- Integrate models into closed-loop simulation
- Own problems end to end through on-vehicle validation
- Build data pipelines, metrics and tooling to evaluate learned versus classical control
Résumé des exigences :
- Graduate degree or bachelor's degree with 4+ years industry experience
- Depth in control theory and dynamic systems
- Hands-on ML on real hardware in the loop
- Production coding in Python and C++
- Experience with PyTorch or similar deep learning frameworks
Outils et compétences
Source et dates
Site carrière / système de candidatures · lever
- Date publiée par l’employeur
- 2026-09-03
- Première détection par notre source
- 2026-10-08
- Mise à jour du dossier source
- 2026-11-07
Ces dates décrivent le dossier source, pas une vérification en direct du recrutement. Consultez l’offre originale.
Détails du poste
| Niveau | Senior |
| Domaine | Développement logiciel |
| Expérience | 4 an(s) et plus |
| Heures par semaine | 40 (estimé) |
| Milieu de travail | Bureau, Véhicule |
| Exigences physiques | physique faible · ordinateur élevé |
| Langues | English |
| Formation | Baccalauréat (exigé), Maîtrise (préféré), Doctorat (préféré) |
| Domaines d’études | Robotics, Controls, Mechanical Engineering, Electrical Engineering, Computer Science |
| Avantages mentionnés | Prime de rendement, Actions ou participation |
Résumé et détails rédigés par l’IA à partir de l’offre; l’offre de l’employeur fait foi.
Waabi
Financement et investisseurs
Tour de financement annoncé: Série C
Investisseur nommé dans le tour: Khosla Ventures, G2 Venture Partners, Uber, NVentures, Volvo Group Venture Capital, Porsche Automobil Holding SE, Radical Ventures, HarbourVest Partners, Linse Capital, Incharge Capital, Export Development Canada, TELUS Global Ventures, BMO Global Asset Management
Date de l’annonce: · Source vérifiée: 2026-11-02
Annonce de l’entrepriseProfil d’entreprise sauvegardé
Renseignements historiques provenant d’un profil The Jobs API sauvegardé (champs LinkedIn/Crunchbase). Ils ne sont pas vérifiés en temps réel; les chiffres peuvent avoir changé. Les valeurs manquantes restent inconnues.
Built to think. Born to haul.
Description complète de l’entreprise
Waabi is pioneering Physical AI – starting with autonomous trucks. Press: [email protected] Business: [email protected]
- Abonnés LinkedIn
- 28 690
- Personnes associées sur LinkedIn
- 291
- Bureaux déclarés
- N/A, Toronto, CA
- San Francisco, California, US
- Catégories d’activité
- Artificial Intelligence
- Transportation
- Automotive
- Artificial Intelligence (AI)
- Machine Learning
- Autonomous Vehicles
- Autres noms enregistrés
- Waabi
- Le profil indique une agence de recrutement
- Non
- Investissement total déclaré
- 1 033 036 757
La devise et la date d’observation ne sont pas fournies; ce n’est ni une valorisation actuelle ni une ronde de financement.
Waabi is pioneering Physical AI – starting with autonomous trucks. Press: [email protected] Business: [email protected]
| Champ | Valeur |
|---|---|
| Secteur | Truck Transportation |
| Taille | 51-200 employees |
| Siège social | Toronto |
| Type d’organisation | Privately Held |
| Fondée en | 2021 |
| Offres sur Jobsy | 54 |
| Voir la page |
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.
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