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 R&D 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 hybrid role (minimum of 1 week per month onsite in Los Angeles) 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 R&D, 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-contrast="none"><span data-ccp-parastyle="heading 3">What Success Looks Like:</span></span></strong><span data-ccp-props="{"134233117":false,"134233118":false,"134245418":true,"134245529":true,"335551550":0,"335551620":0,"335559738":281,"335559739":281}"> </span></p>

<ul>

<li><strong><span data-contrast="none">After 30 Days: </span></strong><span data-contrast="none">You have developed a deep understanding of the product goals, existing infrastructure, and stakeholder requirements. You've conducted technical discovery and proposed a preliminary MLOps architecture—evaluating various ML tools, cloud services, and workflow strategies—clearly outlining pros and cons for each option.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li>

<li><strong><span data-contrast="none">After 60 Days: </span></strong><span data-contrast="none">You’ve delivered a detailed design document that outlines the end-to-end ML pipeline, including data ingestion, model training, deployment, and monitoring. Based on feedback from ML engineers and stakeholders, you’ve iterated on the design and built PoC for the core ML workflow aligned with the approved architecture.</span><span data-ccp-props="{"134233117":false,"134233118":false,"201341983":0,"335551550":1,"335551620":1,"335559737":0,"335559738":240,"335559739":240,"335559740":279}"> </span></li>

<li><strong><span data-contrast="none">After 90 Days: </span></strong><span data-contrast="none">You have delivered the core features of the MLOps pipeline and successfully integrated key tools (e.g., MLflow, SageMaker, or Kubeflow). You’ve also initiated the implementation of the remaining features, ensuring the infrastructure supports scalable, repeatable workflows for model experimentation and deployment in both R&D and production environments.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li>

</ul>

<p><strong><span data-contrast="none"><span data-ccp-parastyle="heading 3">Basic </span><span data-ccp-parastyle="heading 3">Requirements</span><span data-ccp-parastyle="heading 3">:</span></span></strong><span data-ccp-props="{"134233117":false,"134233118":false,"134245418":true,"134245529":true,"335551550":0,"335551620":0,"335559738":281,"335559739":281}"> </span></p>

<ul>

<li><span data-contrast="none">Bachelor’s or higher degree in Computer Science, Machine Learning, or a relevant engineering discipline.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li>

<li><span data-contrast="none">5+ years of experience building large-scale, reliable systems; 2+ years focused on ML infrastructure or MLOps.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li>

<li><span data-contrast="none">Proven experience architecting and deploying production-grade ML pipelines and platforms.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li>

<li><span data-contrast="none">Strong knowledge of ML lifecycle: data ingestion, model training, evaluation, packaging, 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">Hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Airflow, Metaflow, or similar).</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li>

<li><span data-contrast="none">Deep understanding of CI/CD practices applied to ML workflows.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li>

<li><span data-contrast="none">Proficiency in Python, Git, and system design with solid software engineering fundamentals.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li>

<li><span data-contrast="none">Experience with cloud platforms (AWS, GCP, or Azure) and designing ML architectures in those environments.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li>

</ul>

<p><strong><span data-contrast="none"><span data-ccp-parastyle="heading 3">Preferred Qualifications</span><span data-ccp-parastyle="heading 3">:</span></span></strong><span data-ccp-props="{"134233117":false,"134233118":false,"134245418":true,"134245529":true,"335551550":0,"335551620":0,"335559738":281,"335559739":281}"> </span></p>

<ul>

<li><span data-contrast="none">Experience with deep learning architectures (CNNs, RNNs, Transformers) or computer vision.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li>

<li><span data-contrast="none">Hands-on experience with distributed training tools (e.g., PyTorch DDP, Horovod, Ray).</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li>

<li><span data-contrast="none">Background in real-time ML systems and batch inference, including CPU/GPU-aware orchestration.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li>

<li><span data-contrast="none">Previous work in autonomous vehicles, robotics, or other real-time ML-driven systems.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335551550":0,"335551620":0,"335559738":240,"335559739":240}"> </span></li>

</ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p><span style="font-size: 12px;"><em>We are committed to providing fair and transparent compensation in accordance with applicable laws. Salary ranges are listed below and reflect the expected range for new hires in this role, based on factors such as skills, experience, qualifications, and location. Final compensation may vary and will be determined during the interview process. The target hiring range for this position is listed below.</em></span></p></div><div class="title">Target Salary Range:</div><div class="pay-range"><span>$150,000</span><span class="divider">—</span><span>$250,000 USD</span></div></div></div><div class="content-conclusion"><p><span style="font-size: 10pt;"><em>Parallel Systems is an equal opportunity employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to any discriminatory factor protected by applicable federal, state or local laws. We work to build an inclusive environment in which all people can come to do their best work.</em></span></p>

<p><span style="font-size: 10pt;"><em>Parallel Systems is committed to the full inclusion of all qualified individuals. As part of this commitment, Parallel Systems will ensure that persons with disabilities are provided reasonable accommodations. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact your recruiter.</em></span></p></div>