Co-op- Machine Learning Ops

Exol•Wilmington, MA

5d•$32 - $42

About The Position

Symbotic is hiring a Machine Learning Ops Co-op for the Spring 2027 term (January–May). Early Career co-op students contribute to real projects, collaborate with experienced engineers and business leaders, and gain hands-on exposure to technologies behind Symbotic’s robotics and automation platform. Students receive mentorship, professional development, networking opportunities, and events. Transportation and housing benefits are available to students who meet relocation requirements. The role focuses on scaling software and machine-learning capabilities for robotic systems. The co-op will support model deployment, fleet software pipelines, edge computing, training and evaluation workflows, simulation-based testing, diagnostics, compilation architecture, and shared robotic hardware configuration. The position may require up to 10% travel. Employees must have a valid driver’s license and be able to drive and/or fly to client and other customer locations. The employee is responsible for owning a credit card and managing expenses personally, with reimbursement on a bi-weekly basis.

Requirements

Bachelor’s degree or above in computer science, software engineering, robotics engineering, or a related field.
Demonstrated experience with cloud-based infrastructure and services, including Google Cloud Platform (GCP) or Microsoft Azure.
Experience working with complex data systems, including data storage, telemetry data, and camera or sensor data.
Strong software engineering skills and expertise in at least one programming language and its ecosystem.
Ability to deliver high-quality, working code quickly and consistently.
Proficiency in Python or C++.

Responsibilities

Architect and scale the software platform to support current and future autonomous capabilities.
Collaborate with ML R&D teams to ensure new models meet deployment requirements.
Optimize the robotic hardware fleet deployment pipeline, from merge requests through testing and rollout, for systems using ONNX/TensorRT and custom hardware.
Maintain and improve edge computing infrastructure to meet real-time performance and reliability needs.
Create pipelines for training robotic machine-learning systems, including dataset curation, labeling, training, and evaluation metrics.
Use simulation of hardware and mechanics to double the speed and efficiency of robotic system evaluation.
Reduce mechanical hardware maintenance during robotic scale-up using data-driven diagnostics and alert systems.
Engineer robotic software to improve machine-learning performance.
Design efficient compilation architectures that decouple hardware program interdependencies.
Structure Docker Compose configuration to balance robotic hardware sharing.