Senior ML Ops Engineer (Machine Learning Infrastructure)
Parallel Systems · Los Angeles, CA
$150,000–$250,000
Jul 27, 2026
Los Angeles, CA
Jul 28, 2026
What this job asks for AI summary
A Senior MLOps / ML Infrastructure Engineer role at an autonomous rail vehicle startup, responsible for designing and building the end-to-end ML platform that supports autonomy and perception teams. Day-to-day work spans distributed training environments, experiment tracking, CI/CD pipelines for ML workflows, and cloud-based deployment and monitoring infrastructure. The role suits an experienced engineer who has built ML infrastructure from scratch in production settings.
Senior level · 5+ years · Los Angeles-Long Beach-Anaheim, CA · Bachelor's required · Full-time
Pay in the description: $150,000–$250,000
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Roughly 310 people in the Los Angeles-Long Beach-Anaheim, CA area plausibly meet what this posting asks for (software developers). range 230–490
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $164,459 (middle half $130,334–$195,752). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (6)
The role is hybrid (minimum 1 week per month onsite in Los Angeles), not fully remote.
The JD requires 5+ years of large-scale systems experience overall, with 2+ years specifically in ML infrastructure or MLOps; the overall years minimum reflects the broader 5-year gate.
MLflow, Kubeflow, SageMaker, Airflow, and Metaflow are presented as interchangeable examples of required MLOps tooling ('or similar'), so they are grouped as alternatives under a single skill.
AWS, GCP, and Azure are listed as interchangeable cloud platform options in the requirements section.
Deep learning architectures (CNNs, RNNs, Transformers) and computer vision appear under Preferred Qualifications, as does distributed training experience (PyTorch DDP, Horovod, Ray) and autonomous vehicle/robotics background.
SOC confidence is Medium: the role is primarily about building and shipping ML infrastructure software (pointing to 15-1252 Software Developers), but a significant portion of the work involves operating and managing cloud infrastructure, which could also fit 15-1244.
Read the full posting
The employer publishes the full description on their own site — read it there ↗. Or sign in to read it here — it's free, and it also lets you track this application.