Senior ML Ops Engineer
@ Parallel Systems

Los Angeles, California, United States
$195,000
On Site
Full Time
Posted 18 hours ago

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About Parallel Systems

Parallel Systems is pioneering autonomous battery-electric rail vehicles designed to transform freight transportation by shifting portions of the $900 billion U.S. trucking industry onto rail. Our innovative technology improves safety, efficiency, and environmental impact. Join our dynamic team in shaping a smarter, greener future for global freight.

Senior ML Ops Engineer Role

As a Senior ML Ops Engineer at Parallel Systems, you will lead the design and development of scalable ML infrastructure that powers our autonomy and perception pipelines. You will own the ML infrastructure stack from distributed training environments and experiment tracking to deployment and monitoring for both R&D and production.

  • Design and implement robust MLOps solutions including automation pipelines.
  • Architect, deploy, and manage scalable ML infrastructure.
  • Collaborate with ML engineers around data management and deployment.
  • Operate cloud-based systems optimized for ML workloads.
  • Automate model evaluation, selection, and deployment workflows.

What Success Looks Like

In the first 30, 60, and 90 days, you will progressively deepen your understanding of product goals, propose and iterate on MLOps architectures, and integrate critical tools such as MLflow, SageMaker, or Kubeflow to establish scalable, repeatable ML workflows.

Basic Requirements

  • Bachelor’s or higher degree in Computer Science, Machine Learning, or related field.
  • 5+ years experience building large-scale, reliable systems with 2+ years in MLOps or ML infrastructure.
  • Experience with production-grade ML pipelines and lifecycle management.
  • Hands-on expertise with tools such as MLflow, Kubeflow, SageMaker, or Airflow.
  • Proficiency in Python, Git, and system design.
  • Experience with cloud platforms (AWS, GCP, or Azure).

Preferred Qualifications

  • Experience with deep learning architectures or computer vision.
  • Familiarity with distributed training tools like PyTorch DDP, Horovod, or Ray.
  • Background in real-time ML systems and batch inference.
  • Experience in autonomous vehicles or robotics is a plus.

Compensation & Inclusivity

Parallel Systems offers transparent and fair compensation. The target salary range for this position is $150,000 to $240,000 USD. We are an equal opportunity employer committed to diversity and provide reasonable accommodations to candidates who need them.

Key skills/competency

  • MLOps
  • Machine Learning
  • Infrastructure
  • Cloud
  • CI/CD
  • Python
  • Automation
  • Scalability
  • Deployment
  • Distributed Systems

How to Get Hired at Parallel Systems

🎯 Tips for Getting Hired

  • Research Parallel Systems: Understand their autonomous rail initiatives and tech.
  • Tailor Your Resume: Highlight ML infrastructure and MLOps expertise.
  • Emphasize Cloud Skills: Detail your AWS, GCP, or Azure experience.
  • Prepare with Tools: Focus on MLflow, Kubeflow, and CI/CD practices.

📝 Interview Preparation Advice

Technical Preparation

Practice MLflow and Kubeflow setups.
Review cloud platform configurations (AWS/GCP).
Revisit Python and system design fundamentals.
Study distributed training and CI/CD pipelines.

Behavioral Questions

Describe a challenging system deployment experience.
Explain collaboration with cross-functional teams.
Discuss handling project feedback constructively.
Share experiences with time-critical problem solving.

Frequently Asked Questions