Staff Software Engineer, ML Training Infrastructure
Stack AV
Salary undisclosed
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Description
About Stack:
Stack is developing revolutionary AI and advanced autonomous systems designed to enhance safety, reliability, and efficiency of modern operations. Stack's autonomous technology incorporates cutting-edge advancements in artificial intelligence, robotics, machine learning, and cloud technologies, empowering us to create innovative solutions that address the needs and challenges of the dynamic trucking transportation industry. With decades of experience creating and deploying real world systems for demanding environments, the Stack team is dedicated to developing an autonomous solution ecosystem tailored to the trucking industry's unique demands.
If you are an Internal Candidate, please apply via our Internal Job Board.
About the Role:
The ML Training team is dedicated to increasing Stack's AV development velocity by accelerating machine learning iterations. Our core mission is to deliver a training system that is reliable, scalable, user-friendly and observable. We are responsible for the overall AI workflow, ranging from dataset curation to training, validation, acceleration, optimization and deployment of large-scale models that power our autonomous vehicles. In addition, this team is in charge of evangelizing best practices and frameworks among Machine Learning Engineers across the company.
In this Staff role, you will drive the design and development of a high-performance multi-tenant AI training platform. You will balance hands-on coding with long-term technical direction by operating across ML Platform, Infrastructure, Autonomy and Safety Evaluation teams to accelerate the development of autonomous vehicles across Stack.
Responsibilities:
- Design & evolve high-performance training platform components including orchestration, training abstractions, control plane, observability and performance tuning
- Deliver end-to-end ML model pipelines across logs processing, feature extraction, dataset schema design/storage, model configuration management, model training, and profiling/acceleration workflows.
- Analyze training infrastructure performance to identify and resolve performance bottlenecks
- Evangelize system abstractions and tooling that enable MLEs to rapidly iterate on models
- Incorporate OSS tools to enable ML engineers self-sufficiently profile and optimize their workflows
- Promote Engineering Excellence: Maintain a high bar for engineering excellence in their own work but also set a culture of engineering excellence within the team
Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
- 6+ years of experience with ML Platforms and building ML-based applications (modeling experience is a bonus).
- Strong programming skills in Python
- Prior experience with Lance, PyTorch, Ray Data or equivalent technologies
- Proven track record of building scalable, reliable infra in a fast-paced environment while working with MLEs across multiple modeling teams
- A deep understanding of design tradeoffs and ability to articulate those tradeoffs to build alignment across XFN teams
- Experience with model training, model optimization, or large-scale data processing pipelines
- Autonomous vehicles (AV) experience is a bonus
- Strong analytical and problem-solving skills
- Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders
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Please Note: Pursuant to its business activities and use of technology, Stack AV complies with all applicable U.S. national security laws, regulations, and administrative requirements, which can restrict Stack AV’s ability to employ certain persons in certain positions pursuant to a range of national security-related requirements. As such, this position may be contingent upon Stack AV verifying a candidate’s residence, U.S. person status, and/or citizenship status. This position may also involve working with software and technologies subject to U.S. export control regulations. Under these regulations, it may be necessary for Stack AV to obtain a U.S. government export license prior to releasing its technologies to certain persons. If Stack AV determines that a candidate’s residence, U.S. person status, and/or citizenship status will require a license, prohibit the candidate from working in this position, or otherwise be subject to national security-related restrictions, Stack AV expressly reserves the right to either consider the candidate for a different position that is not subject to such restrictions, on whatever terms and conditions Stack AV shall establish in its sole discretion, or, in the alternative, decline to move forward with the candidate’s application.
About this role
Stack AV is building an ML training infrastructure platform that directly accelerates the development of autonomous vehicles for trucking. As a Staff Software Engineer on this team, you'd own the design and evolution of a multi-tenant training system handling the full ML lifecycle—from dataset curation and feature extraction through model training, optimization, and deployment at scale. This is a hands-on technical leadership role where you'll balance writing code with setting long-term direction across ML Platform, Infrastructure, Autonomy, and Safety Evaluation teams.
The work centers on solving real infrastructure challenges: designing orchestration and control plane components, identifying and fixing performance bottlenecks, and building abstractions that let machine learning engineers iterate faster. You'll also be responsible for promoting engineering excellence across the team and evangelizing best practices through tooling and frameworks. Success requires deep expertise in ML platforms and large-scale data pipelines, strong programming ability in Python or C++, and hands-on experience with technologies like PyTorch, Ray Data, or Lance.
This role suits someone with 6+ years building ML infrastructure in fast-paced environments who can articulate technical tradeoffs to cross-functional teams and thrives on both individual contribution and mentorship. AV experience is a bonus but not required. The position is remote and based in Pittsburgh.
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