Software Engineer

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 (MLEs) 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.