Senior ML Ops Engineer (Machine Learning Infrastructure)

reputed company Systems is pioneering autonomous battery-electric rail vehicles designed to reputed company 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. 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 scale. You'll collaborate closely with world-class engineers in autonomy, robotics, and software, helping shape the core 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 each option. • 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 core ML workflow reputed company with the approved architecture. • After 90 Days: 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 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-scale, 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 applied 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 computer 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-aware 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 the workplace. reputed company reputed company applicants will receive consideration for employment without regard to any discriminatory reputed company protected by applicable federal, state or local laws. We work to build