Staff Software Engineer, ML Training Infrastructure
Stack AV
full-time
Posted on:
Location Type: Remote
Location: Pennsylvania • United States
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Job Level
About the role
- In the ML Training, our mission is to provide a reliable, scalable, and easy to use training framework for modeling needs of Stack AV.
- In addition, this team is responsible for the overall developer experience of ML engineers including building tools for testing, validation, and understanding models and the data used to train them.
- Finally, we are responsible for model optimization and deployment.
Requirements
- Experience with both ML Platforms and building ML-based applications.
- Experience building scalable, reliable infra at a fast-paced environment working with MLEs on several different modeling teams.
- A deep understanding of design tradeoffs and ability to articulate those tradeoffs and work with others on getting alignment.
- Experience with building ML models or ML infra in the domains of autonomous vehicles, perception, and decision making (desirable but not required).
- Experience with model training, model optimization, or large data processing pipelines.
- Built an end to end ML model pipeline including components such as logs processing, feature extraction, dataset storage, model configuration management, model training, experiment frameworks, and serving deployment.
- Shipped ML products (NLP, computer vision, recommender systems, etc.) at scale to make business impact
- Knows how to build appropriate abstractions and tooling to ensure MLEs are able to rapidly iterate on models.
- Prior AV experience
Benefits
- We are proud to be an equal opportunity workplace. We believe that diverse teams produce the best ideas and outcomes. We are committed to building a culture of inclusion, entrepreneurship, and innovation across gender, race, age, sexual orientation, religion, disability, and identity.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
ML platformsML-based applicationsmodel trainingmodel optimizationlarge data processing pipelinesend to end ML model pipelinefeature extractiondataset storagemodel configuration managementexperiment frameworks
Soft Skills
design tradeoffsarticulation of tradeoffsalignment with othersrapid iteration