Machine Learning Engineer, Kodiak Robotics
Deployment
$200-265k
+ Equity and annual bonuses
More information about location
Self-driving technology for long-haul trucks
Be an early applicant
Self-driving technology for long-haul trucks
201-500 employees
Be an early applicant
$200-265k
+ Equity and annual bonuses
More information about location
201-500 employees
Company mission
To carry freight forward - so people, partners, and the planet thrive.
Role
Who you are
- MS or PhD in Computer Science, Robotics, Machine Learning, or a related technical field, or equivalent practical experience
- Strong experience deploying machine learning or deep learning systems in production environments
- Proficiency in Python, C++ and modern ML frameworks such as PyTorch or TensorFlow
- Experience working with large-scale distributed training systems
- Solid understanding of optimization, and machine learning fundamentals
- Strong software engineering fundamentals including testing, debugging, and system design
- Ability to work collaboratively across teams to solve complex autonomy and robotics challenges
What the job involves
- Kodiak is seeking Machine Learning Engineers focussing on model deployment to help build the intelligence that powers the Kodiak Driver. Our ML teams work across perception, prediction, planning, and AI infrastructure to turn real-world driving data into models that enable safe and scalable autonomous trucking
- In this role, you will design and deploy machine learning systems that improve our vehicles’ ability to understand the world, predict the behavior of other road users, and make safe driving decisions. You will collaborate closely with robotics, autonomy, and infrastructure teams to continuously improve the performance of our autonomy stack using large-scale data from our growing fleet
- This is a high-impact opportunity to work on cutting-edge AI systems operating in the real world, where every mile driven improves our models and brings autonomous trucking closer to global deployment
- Help design, train, with a focus towards deploying onboard machine learning models that improve the performance/latency of the Kodiak autonomy stack which includes quantization, pruning, converting to ONNX/TensorRT, custom GPU kernels and profiling
- Help identify and achieve parity between onboard and deployed offboard models using validation on HIL
- Collaborate with cross-functional teams including perception, planning, simulation, and autonomy infrastructure to integrate ML models into production systems
- Analyze real-world driving logs to identify edge cases and improve model robustness and safety
- Use profiling tooling to additionally help improve the train time for models by identifying and removing bottlenecks
- Contribute to the development of scalable AI infrastructure that supports continuous learning and deployment across Kodiak’s fleet
Our take
Global supply chains rely on frictionless long-haul trucking freight networks, but the repetitive and stationary nature of the job has resulted in a lack of willing drivers. Whilst this has been partially resolved through the introduction of self-driving trucks, such systems are notoriously difficult to maintain which ultimately leads to significant downtime.
Kodiak Robotics develops self-driving technology for long-haul trucks, providing wing mirrors built with sensors called Kodiak SensorPods. These sensors provide the technology necessary for the trucks to be driven safely and autonomously and have been designed to be replaced or serviced rapidly to minimize downtime.
odiak Robotics has taken a unique approach by recognizing the high-maintenance nature of existing self-driving systems, innovating upon this through its easily replaceable SensorPods. Through this approach and several successful rounds of funding, the company aims to fill empty truck driving positions with safer AI drivers.
Steph
Company Specialist at Welcome to the Jungle
Insights
21% employee growth in 12 months
Company
Funding (last 2 of 3 rounds)
Sep 2023
$75m
CONVERTIBLE
Nov 2021
$125m
SERIES B
Total funding: $240m
Company benefits
- Remote-friendly work environment
- 401k
- Generous PTO policy
- Life insurance
- Assortment of medical (PPO/HMO/HDHP), dental, vision, and FSA plans
- Family-friendly company events
- Dog-friendly office
- Employee-driven fitness classes
- Beautiful, renovated facilities in Mountain View, CA and Lancaster, TX
- Free EV charging
- Trivia and board game nights
Company HQ
Mountain View, CA
Leadership
Don Burnette
(CEO & Founder)Experience as Co-founder of Otto. Software Technical Lead at Uber from 2016 to 2018.
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