Senior / Staff ML Training Optimization Engineer, Waabi

$141-249k

+ Equity incentive awards + Annual performance bonus. Salary is benchmarked based on US rates and may vary depending on the location

Python

C++

Rust

CUDA

PyTorch

Bazel

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

$141-249k

+ Equity incentive awards + Annual performance bonus. Salary is benchmarked based on US rates and may vary depending on the location

Python

C++

Rust

CUDA

PyTorch

Bazel

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 Bachelors degree with a minimum of 4 years of industry experience in Computer Science, Robotics and/or similar technical field(s) of study
Solid coding proficiency in a variety of coding languages including Python, C++ or Rust
Experience in deep learning frameworks such as PyTorch or Jax
Skilled in profiling CPU and GPU code using tools such as PyTorch Profiler and NVIDIA Nsight
Open-minded and collaborative team player with willingness to help others
Passionate about self-driving technologies, solving hard problems, and creating innovative solutions

Desirable

Experience in identifying when custom CUDA kernels are needed, and implementing them
Experience in Bazel in a monorepo environment, and integrating third party packages into dev environments
Experience with Kubernetes-based training platforms

What the job involves

Build standardized distributed training frameworks for research and production, drive our training towards new levels of stability and efficiency
Comprehensively profile model runtime and memory to pinpoint performance bottlenecks
Identify and evaluate emerging technologies that can be adopted into Waabi’s training and inference frameworks. Examples include designing new CUDA kernels, quantization-aware training and inference, and compilation/deployment techniques
Work with researchers and ML engineers on best-practices for optimal resource usage
Create and improve tooling and dashboards to ensure broad adoption of your work

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 too, with is a major boost for its future. The two companies have co-developed a new Volvo VNL Autonomous truck that pairs VOlvo's purpose-built autonomy platform with Waabi's end-to-end AI driving system, sensors, and compute. Volvo has also backed Waabi financially through its venture arm, giving Waabi both manufacturing support and long-term investment. Toget, erhjtis puts Waabi in a position to scale and move towards fully driverless trucking quicker.

Kirsty

Company Specialist at Welcome to the Jungle

Insights

Led by a woman

Top investors

81% employee growth in 12 months

Glassdoor (4.2)

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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