Senior Staff Software Engineer, Waabi

ML-based Controls

$241-320k

+ equity incentive awards and an annual performance bonus

Python

C++

PyTorch

Mid and Senior level

San Francisco Bay Area

Toronto

Remote from Canada, US

AI-powered self-driving technology

Be an early applicant

AI-powered self-driving technology

201-500 employees

B2BTravelCarsAugmented RealitySaaS

Be an early applicant

$241-320k

+ equity incentive awards and an annual performance bonus

Python

C++

PyTorch

Mid and Senior level

San Francisco Bay Area

Toronto

Remote from Canada, US

201-500 employees

B2BTravelCarsAugmented RealitySaaS

Company mission

To bring commercially viable self-driving technology to society.

Job

Company

Role

Who you are

MS/PhD or Bachelor's degree with a minimum of 4 years of industry experience in Robotics, Controls, Mechanical/Electrical Engineering, Computer Science and/or similar technical fields
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

What the job involves

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

Our take

Autonomous vehicle technology is rapidly developing, and many sectors are recognizing its value. However, whilst the self-driving industry has made significant advancements there is much more that can be done. There are challenges in this space surrounding scale and developing commercially viable self-driving technology.

Waabi hopes to tackle these challenges and be the leader in self-driving technology with their "AI first approach". The company said that combined with a closed-loop simulator, this approach will allow both common driving scenarios and safety-critical edge cases to be tested at scale - solving one of the key issues in the industry.

Waabi has a growing partnership with Volvo, which has seen the two companies develop an autonomous truck together. Volvo has also backed Waabi financially, as have other investors - with the company raising a whopping $1 billion in 2026. This capital will be used to expand into robotaxis through a new partnership with Uber.

Kirsty

Company Specialist at Welcome to the Jungle

Insights

Led by a woman

Top investors

81% employee growth in 12 months

Glassdoor (4.5)

Company

Funding (last 2 of 3 rounds)

Jan 2026

$750m

SERIES C

Jun 2024

$200m

SERIES B

Total funding: $1.0bn

Leadership

Raquel Urtasun

(Founder & CEO)

Previously worked at Uber as Chief Scientist, Head of Uber ATG R&D. Is also a professor at the University of Toronto.

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