9 hours ago
Responsibilities
- Design and evolve high-performance ML training platform components, including orchestration, training abstractions, control plane, observability, and performance tuning.
- Deliver end-to-end ML model pipelines covering log processing, feature extraction, dataset schema design and storage, model configuration, training, and profiling or acceleration workflows.
- Analyze training infrastructure performance and resolve bottlenecks.
- Develop and evangelize system abstractions and tooling that enable machine learning engineers to iterate rapidly and work self-sufficiently.
- Set and promote a culture of engineering excellence across the team.
- Provide hands-on coding and long-term technical direction across ML Platform, Infrastructure, Autonomy, and Safety Evaluation teams.
Requirements
- 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.
- Strong programming skills in Python, C++, or equivalent.
- Prior experience with Lance, PyTorch, Ray Data, or equivalent technologies.
- Experience building scalable, reliable infrastructure and working with machine learning engineers across multiple modeling teams.
- Experience with model training, model optimization, or large-scale data processing pipelines.
- Strong understanding of design tradeoffs and ability to build alignment across cross-functional teams.
- Strong analytical, problem-solving, verbal communication, and written communication skills.
- Modeling experience and autonomous vehicle experience are bonuses.
Categories
About Stack AV
Stack AV builds autonomous driving software and integrated systems for commercial trucking, combining AI perception, planning, robotics, and cloud-based fleet operations. The company works with freight carriers and logistics operators to pilot and deploy autonomy-enabled trucking solutions and supporting services. Founded in 2023 and headquartered in Pittsburgh, PA, it is a privately held firm focused on long-haul and hub-to-hub routes.