Senior ML Ops Engineer (Machine Learning Infrastructure)

Join Parallel Systems, a company focused on building the first fully autonomous, battery-electric rail vehicles. As a Senior ML Ops Engineer, you will lead the design and development of scalable systems that power our autonomy and perception pipelines. You will have full ownership of the ML infrastructure stack and collaborate closely with world-class engineers in autonomy, robotics, and software. This is a hybrid role with a minimum of 1 week per month onsite in Los Angeles.

• Design and implement robust MLOps solutions, including automated pipelines for data management, model training, deployment and monitoring.
• Architect, deploy, and manage scalable ML infrastructure for distributed training and inference.
• Collaborate with ML engineers to gather requirements and develop strategies for data management, model development and deployment.
• Proficiency in Python, Git, and system design with solid software engineering fundamentals
• Deep understanding of CI/CD practices applied to ML workflows
• Experience with cloud platforms (AWS, GCP, or Azure) and designing ML architectures in those environments
• Bachelor’s or higher degree in Computer Science, Machine Learning, or a relevant engineering discipline
• Strong knowledge of ML lifecycle: data ingestion, model training, evaluation, packaging, and deployment
• Proven experience architecting and deploying production-grade ML pipelines and platforms
• 5+ years of experience building large-scale, reliable systems; 2+ years focused on ML infrastructure or MLOps
• Hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Airflow, Metaflow, or similar)
• Experience with deep learning architectures (CNNs, RNNs, Transformers) or computer vision
• Hands-on experience with distributed training tools (e.g., PyTorch DDP, Horovod, Ray)
• Background in real-time ML systems and batch inference, including CPU/GPU-aware orchestration
• Previous work in autonomous vehicles, robotics, or other real-time ML-driven systems