Senior Machine Learning Engineer, Stack AV
Auto Labeling
Autonomous systems for the transportation industry
Open for applications
Autonomous systems for the transportation industry
201-500 employees
Open for applications
201-500 employees
Company mission
To revolutionize the transportation industry through the deployment of advanced autonomous systems.
Role
Who you are
- Degree in Computer Science, Machine Learning, Robotics, or related field
- Master's or PhD preferred
- 3+ years building ML systems
- Experience with distributed training systems
- Autonomous vehicle space experience is a plus
- Strong Python engineering skills, particularly PyTorch
- Strong experience with GPUs; CUDA experience is a plus
- Ability to work in a fast-paced startup environment while maintaining high-quality standards
What the job involves
- The Auto Labeling team at Stack develops large ML models. These models leverage state-of-the-art multi-sensor fusion techniques, integrating data from lidars, cameras, radars, and IMUs. The goal is to generate high-quality labeled data, essential for training and evaluating Stack's onboard perception models.
- This role offers the chance to make significant design contributions, work with extensive autonomous vehicle datasets, and collaborate with various onboard and offboard consumers of labeled data
- Design, evaluate, and deploy acausal large ML models for high-quality labeled data generation from multi-sensor inputs
- Work with state-of-the-art techniques in large-scale distributed model training and evaluation
- Collaborate with onboard and offboard downstream consumers throughout the development lifecycle, from requirements gathering to evaluation and ongoing communication
- Contribute to the overarching vision of building safety-critical and robust onboard and offboard perception systems
- Uphold Stack’s culture of engineering excellence
Our take
Driver shortages, decelerating efficiency and broad safety concerns have plagued the US trucking industry for some time now. High operating costs paired with increasingly scrutinized high emissions are leading to concern over the future of the logistics and goods transport market. Stack AV’s autonomous trucking solution aims to lighten the load of these issues, using AI to create smart supply chains and autonomous driving systems.
Its founding team have long been exploring the possibilities of autonomous transport through combined experience with Uber, and Google smart cars and shared founding roles at Argo AI, a similar company that disbanded in 2022 after it failed to become profitable for shareholders. Hopefully, however, the launch of Stack AV will balance the decades of experience as well as hard lessons learnt from the failed predecessor company and will find new ways to make autonomous driving tech a sustainable investment.
With a staggering $1 billion invested in Stack AV by SoftBank in 2023, the company is well-armoured for the time being. Its main competitor is Aurora, which underwent a successful IPO in 2021 but never achieved the high level of early funding that Stack AV did. It’s a promising start then for the Argo AI veterans' new venture.
Kirsty
Company Specialist at Welcome to the Jungle
Company
Funding (1 round)
Sep 2023
$1bn
SERIES A
Total funding: $1bn
Company HQ
Downtown, Pittsburgh, PA
Leadership
Brian Salesky
(CEO)Previously CEO of Argo AI, Director of Hardware Development for self-driving cars at Google, Principal Commercialization Specialist at the National Robotics Engineering Center, and Technical Program Manager at Google.
Peter Rander
(President)Previously President and Co-Founder at Argo AI, Engineering Lead at Uber and Principal Commercialization Specialist at the National Robotics Engineering Center.
Brett Browning
(CTO)Previously CTO at Argo AI, Senior Engineering Manager at Uber Advanced Technologies Center, Co-Founder at Mine Vision Systems and a Senior Systems Scientist at Carnegie Mellon University.
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