<div class="content-intro"><p>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.</p></div><h1><span style="font-size: 12pt;">About The Role</span></h1> <p><span style="font-size: 10pt;">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 <em>architect</em> and <em>build</em> 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.</span></p> <p><span style="font-size: 10pt;">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. </span></p> <h2><span style="font-size: 12pt;">Key Responsibilities</span></h2> <p><span style="font-size: 10pt;"><em>Execution and Technical Ownership</em></span></p> <ul> <ul> <li style="font-size: 10pt;"><span style="font-size: 10pt;">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 </span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Develop reliable workflows across cloud compute, Kubernetes, and continuous automation</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Build core infrastructure components such as the model registry, feature store and experiment tracking tooling.</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Own developer-facing APIs and CLI tools that make ML workflows simple and reproducible. </span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Implement the CI/CD lifecycle for ML that enable continuous retraining, automated testing, and seamless model delivery to production environments</span></li> </ul> </ul> <p><span style="font-size: 10pt;"><em>Collaboration</em></span></p> <ul> <li> <ul> <li style="font-size: 10pt;"><span style="font-size: 10pt;">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.</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Partner with data platform engineers to integrate ML orchestration and metadata tracking tools with our existing data lake and pipelines.</span></li> </ul> </li> </ul> <p><span style="font-size: 10pt;"><em>Engineering Excellence, Growth and Impact</em>: </span></p> <ul> <li> <ul> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Apply MLOps best practices: reproducibility, lineage, rollback, monitoring and governance. </span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Mentor junior engineers and influence the broader cloud platform organization’s roadmap.</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Contribute to internal discussions on platform architecture, reliability, and scalability alongside the broader ML and data platform team</span></li> </ul> </li> </ul> <h2><span style="font-size: 10pt;">What We’re Aiming For (MLOps Level 2)</span></h2> <ul> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Version-controlled ML pipelines (data, code, and config)</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Automated and reproducible model training and evaluation</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Continuous integration and delivery for ML workflows</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Centralized experiment tracking and performance visualization</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Standardized model packaging and deployment to production</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Monitoring of models post-deployment</span></li> </ul> <h2><span style="font-size: 12pt;">Required Qualifications</span></h2> <ul> <li style="font-size: 10pt;"><span style="font-size: 10pt;">5+ years of software engineering experience, with at least 2+ years working on ML infrastructure, data platforms or MLOps systems in production environments.</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">Experience building and maintaining components of modern ML platforms—such as experiment tracking, model registries, training pipelines, or deployment systems</span></li> <li style="font-size: 10pt;"><span style="font-size: 10pt;">