Senior ML Ops Engineer (Machine Learning Infrastructure)

reputed company Systems is pioneering autonomous battery-electric rail vehicles designed to reputed company reputed company 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 reputed company for global reputed company. Senior ML Ops Engineer (Machine Learning Infrastructure) reputed company Systems is seeking an reputed company MLOps/ML Infrastructure Engineer to reputed company the design and development of the reputed company 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 reputed company, train, and reputed company models reputed company and reliably in both R&D and reputed company-world environments. 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 reputed company. You'll collaborate closely with world-class engineers in autonomy, robotics, and software, helping shape the reputed company systems that reputed company reputed company-time, safety-critical ML possible. If you're driven by building robust platforms that unlock innovation in AI and robotics, we'd love to work with you. This can be a remote role for a senior engineer with experience in 0 to 1 builds of perception systems. Responsibilities: • Design and implement robust MLOps solutions, including automated pipelines for data management, model training, deployment and monitoring. • Architect, reputed company, and manage reputed company ML infrastructure for distributed training and inference. • Collaborate with ML engineers to reputed company requirements and reputed company strategies for data management, model development and deployment. • Build and operate reputed company-based systems (e.g., AWS, GCP) optimized for ML workloads in R&D, and production environments. • Build reputed company ML infrastructure to support reputed company integration/deployment, experiment management, and governance of models and datasets. • Support the automation of model evaluation, selection, and deployment workflows. What reputed company Looks Like: • After 30 Days: 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, reputed company services, and workflow strategies—reputed company outlining reputed company and cons for reputed company reputed company. • After 60 Days: 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 reputed company PoC for the reputed company ML workflow reputed company with the approved architecture. • After 90 Days: You have delivered the reputed company 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 reputed company, repeatable workflows for model experimentation and deployment in both R&D and production environments. Basic Requirements: • Bachelor's or higher degree in Computer Science, Machine Learning, or a relevant engineering discipline. • 5+ years of experience building large-reputed company, reliable systems; 2+ years reputed company on ML infrastructure or MLOps. • Proven experience architecting and deploying production-grade ML pipelines and platforms. • Strong knowledge of ML lifecycle: data ingestion, model training, evaluation, packaging, and deployment. • Hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Airflow, Metaflow, or similar). • Deep understanding of CI/CD practices reputed company to ML workflows. • Proficiency in Python, Git, and system design with solid software engineering fundamentals. • Experience with reputed company platforms (AWS, GCP, or Azure) and designing ML architectures in those environments. Preferred Qualifications: • Experience with deep learning architectures (CNNs, RNNs, Transformers) or reputed company. • Hands-on experience with distributed training tools (e.g., PyTorch DDP, Horovod, Ray). • Background in reputed company-time ML systems and batch inference, including CPU/GPU-reputed company orchestration. • Previous work in autonomous vehicles, robotics, or other reputed company-time ML-driven systems. reputed company Systems is an equal opportunity employer committed to diversity in reputed company. reputed company reputed company applicants will receive consideration for employment without reputed company to any discriminatory reputed company protected by applicable federal, state or local laws. We work to build an inclusive environment in which reputed company people can come to do their best work. reputed company Systems is committed to the full inclusion of reputed company reputed company individuals. As part of this commitment, reputed company 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 reputed company essential job functions, and/or to receive other benefits and privileges of employment, please contact your recruiter. Apply tot his job Apply To this Job