Senior ML Ops Engineer (Machine Learning Infrastructure)
ParallelAI Agents company
Los Angeles, United States$150,000 - $250,000 USDSenior
Khosla Ventures
Index Ventures
First Round Capital
Sequoia Capital
Data & AI
About the role
TL;DR
Lead ML Ops Engineer to build scalable ML infrastructure for autonomous rail vehicles.
- •Parallel Systems is seeking an experienced MLOps/ML Infrastructure Engineer to lead the design and development of scalable systems for their autonomy and perception pipelines.
- •This role is crucial for enabling ML teams to efficiently develop, train, and deploy models in both R&D and real-world environments.
- •Key Responsibilities 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.
- •Build and operate cloud-based systems optimized for ML workloads.
- •Build scalable ML infrastructure to support continuous integration/deployment, experiment management, and governance of models and datasets.
- •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 focused on ML infrastructure or MLOps.
- •Proven experience architecting and deploying production-grade ML pipelines and platforms.
- •Hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Airflow, Metaflow, or similar).
- •Proficiency in Python, Git, and system design with solid software engineering fundamentals.
Required skills
PythonGitMLflowKubeflowSageMakerAirflowCI/CDDockerKubernetesAWSGoogle CloudAzureComputer Vision
Nice-to-have skills
PyTorchNLPLLMs
Domain expertise
logisticsautomotivedeeptech
Tech stack
PythonGitAWSGoogle CloudAzureMLflowKubeflowSageMakerAirflowCI/CDDockerKubernetesPyTorchComputer Vision
