Senior ML Ops Engineer (Machine Learning Infrastructure)

<div class="content-intro"><p>Parallel Systems is pioneering autonomous battery-electric rail vehicles designed to transform freight transportation by shifting portions of the $900 billion U.S. trucking industry onto rail. Our innovative technology offers cleaner, safer, and more efficient logistics solutions. Join our dynamic team and help shape a smarter, greener future for global freight.</p></div><p><strong><span data-contrast="none"><span data-ccp-parastyle="heading 1">Senior </span><span data-ccp-parastyle="heading 1">ML Ops Engineer</span></span><span data-ccp-props="{"134245418":true,"134245529":true,"335559738":360,"335559739":80}"> (Machine Learning Infrastructure)</span></strong></p> <p><span data-contrast="auto">Parallel Systems is seeking an experienced MLOps/ML Infrastructure Engineer to lead the design and development of the scalable systems that power our autonomy and perception pipelines. As we build the first fully autonomous, battery-electric rail vehicles, you will play a critical role in enabling the ML teams to develop, train, and deploy models efficiently and reliably in both RD and real-world environments. </span></p> <p><span data-contrast="auto">This is an opportunity to take full ownership of the ML infrastructure stack, from distributed training environments and experiment tracking to deployment and monitoring at scale. You’ll collaborate closely with world-class engineers in autonomy, robotics, and software, helping shape the core systems that make real-time, safety-critical ML possible. If you're driven by building robust platforms that unlock innovation in AI and robotics, </span><span data-contrast="none">we’d love to work with you.</span><span data-ccp-props="{"134233117":false,"134233118":false,"134245418":true,"134245529":true,"201341983":0,"335551550":1,"335551620":1,"335559685":0,"335559737":0,"335559738":0,"335559739":160,"335559740":279}"> </span></p> <p><span data-ccp-props="{"134245418":true,"134245529":true}">This can be a remote role for a senior engineer with experience in 0 to 1 builds of perception systems. </span></p> <p><strong><span data-contrast="none"><span data-ccp-parastyle="heading 3">Responsibilities:</span></span></strong></p> <ul> <li><span data-contrast="none">Design and implement robust MLOps solutions, including automated pipelines for data management, model training, deployment and monitoring.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li> <li><span data-contrast="none">Architect, deploy, and manage scalable ML infrastructure for distributed training and inference.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li> <li><span data-contrast="none">Collaborate with ML engineers to gather requirements and develop strategies for data management, model development and deployment.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li> <li><span data-contrast="none">Build and operate cloud-based systems (e.g., AWS, GCP) optimized for ML workloads in RD, and production environments.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li> <li><span data-contrast="none">Build scalable ML infrastructure to support continuous integration/deployment, experiment management, and governance of models and datasets.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li> <li><span data-contrast="none">Support the automation of model evaluation, selection, and deployment workflows.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li> </ul> <p><strong><span data-contras ... (truncated, view full listing at source)