Senior Software Engineer, AI/ML Platform

Agility’s commercially deployed humanoids operate alongside teams in warehouses, manufacturing facilities, and distribution centers—tackling physically demanding and repetitive tasks while enabling workers to focus on higher-value work. With industry-leading safety standards and years of proven deployment data, we're pioneering a new era of automation that enhances human potential.

Join the team building the machine learning platform to power fleet-scale humanoid robotics. As a senior engineer on the ML Infrastructure and Platform group, you will help architect and build the foundational infrastructure for AI and machine learning operations at Agility. This includes the platform layer for data collection and processing, training, sim and real evaluation, and model management and observability.

Your work will empower our AI teams across perception, controls, skills, and innovation to build and deploy next-generation robot foundation models and end-to-end policies for humanoid robots by providing tools to develop and operationalize machine learning at scale.

Execution and Technical Ownership

• Contribute to the design and implementation of the ML platform for orchestrating the end to end AI flywheel of data processing, training, evaluation, and deployment

• Develop reliable workflows across cloud compute, Kubernetes, and continuous automation

• Build core infrastructure components such as the model registry, feature store and experiment tracking tooling.

• Own developer-facing APIs and CLI tools that make ML workflows simple and reproducible.

• Implement the CI/CD lifecycle for ML that enable continuous retraining, automated testing, and seamless model delivery to production environments

Collaboration

• Work closely with the Staff ML Infra Engineer and cross-functional stakeholders (AI researchers and robotics engineers) to understand requirements and translate them into scalable solutions/systems.

• Partner with data platform engineers to integrate ML orchestration and metadata tracking tools with our existing data lake and pipelines.

• Apply MLOps best practices: reproducibility, lineage, rollback, monitoring and governance.

• Mentor junior engineers and influence the broader cloud platform organization’s roadmap.

• Contribute to internal discussions on platform architecture, reliability, and scalability alongside the broader ML and data platform team

What We’re Aiming For (MLOps Level 2)

• Version-controlled ML pipelines (data, code, and config)

• Automated and