Staff AI Software Engineer, AI Core

About The Role

The AI Engineering team builds the models and infrastructure that enable Digit to operate autonomously, efficiently, and safely. As a Staff AI Core Engineer, you will lead the development of the foundational tools, libraries, and data systems that power our AI teams to deliver robust and scalable learning-based capabilities for Digit.

This is a remote-friendly role with a preference for candidates located near one of our offices for in-person collaboration, meetings, and events.

 

About The Work

• Develop data pipelines and curation workflows to maintain high-quality datasets for perception, locomotion, and manipulation tasks.
• Design, build, and maintain scalable AI infrastructure, including shared libraries, model training pipelines, and evaluation frameworks.
• Build dashboards, visualization tools, and reusable metrics to monitor and improve model performance and operational readiness.
• Define and implement testing, validation, and reproducibility tools to ensure reliable deployment of models on Digit.
• Collaborate with skills and control teams to integrate models efficiently into production systems, including real-time execution environments.
• Promote best practices for data management, experiment tracking, and ML operations across the AI Engineering organization.

 

About You

• 8+ years of professional experience in AI infrastructure, ML engineering, or MLOps roles.
• Strong software engineering skills, particularly in Python; proficiency in developing scalable, maintainable libraries and tools.
• Experience building and maintaining ML pipelines, from dataset curation to training, evaluation, and deployment.
• Familiarity with training frameworks such as PyTorch or TensorFlow.
• Practical understanding of data-driven model development, including dataset management, reproducibility, and performance monitoring.
• Excellent communication skills and a collaborative mindset for working across AI, controls, and robotics software teams.

 

Bonus Qualifications

• Experience deploying machine learning models into real-time or embedded systems.
• Experience managing cloud or on-premise GPU clusters, distributed training, or high-performance compute environments.
• Familiarity with experiment tracking tools (e.g. MLflow, Weights & Biases) and orchestration systems (e.g.Kubernetes).
• Background in robotics, perception, or control system applications.