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Senior ML Ops Engineer (Machine Learning Infrastructure)
Summary by AIEngineer.careers
Senior ML Ops Engineer (Machine Learning Infrastructure) is a MLOps Engineer role (full-time). with Parallel. in LOS ANGELES, US. Compensation shown: $150K–$250K. Imported listing (source: workingreen.jobs). Apply on the employer's site (workingreen.jobs).
Imported listing (source: workingreen.jobs) · Apply on workingreen.jobs
LOS ANGELES · US
$150K–$250K
Full-time
SENIOR
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from workingreen.jobs
Parallel Systems is seeking a Senior MLOps Engineer to lead the design and development of scalable ML infrastructure for autonomous battery-electric rail vehicles. The role involves building robust MLOps pipelines, managing distributed training and inference, and collaborating with ML and robotics teams. This is a hybrid position with at least one week per month onsite in Los Angeles.
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 (e.g., AWS, GCP) optimized for ML workloads in R&D and production environments.
- Build scalable ML infrastructure to support continuous integration/deployment, experiment management, and governance of models and datasets.
- Support the automation of model evaluation, selection, and deployment workflows.
Requirements
- Bachelor’s or higher degree in Computer Science, Machine Learning, or a relevant engineering discipline.
Parallel
Palo Alto · US · 73+ employees
Parallel (also known as Parallel Web Systems) provides AI-focused web infrastructure and API platforms for search, data extraction, and knowledge retrieval. The company enables AI agents to perform real-time research, monitor web content, and reason over web-scale information using their proprietary index.
