Senior ML Ops Engineer (Machine Learning Infrastructure)

Parallel

Los Angeles, CA Full Time USD 150,000 - 250,000 6 days ago

About The Job

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 offers cleaner, safer, and more efficient logistics solutions. Join our dynamic team and help shap...

Senior ML Ops Engineer (Machine Learning Infrastructure)

Parallel Systems is seeking an experienced MLOps/ML Infrastructure Engineer to lead the design and development of the scalable systems that power our autonomy and perception pipelines. As we build the first fully autonomous, battery-electric rail vehicles, you will play a critical role in enabling the ML teams to de...

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 (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.

After 30 Days: You have developed a deep understanding of the product goals, existing infrastructure, and stakeholder requirements. You've conducted technical discovery and proposed a preliminary MLOps architecture—evaluating various ML tools, cloud services, and workflow strategies—clearly outlining pros and cons for each option.

After 60 Days: You’ve delivered a detailed design document that outlines the end-to-end ML pipeline, including data ingestion, model training, deployment, and monitoring. Based on feedback from ML engineers and stakeholders, you’ve iterated on the design and built PoC for the core ML workflow aligned with the approved architecture.

After 90 Days: You have delivered the core features of the MLOps pipeline and successfully integrated key tools (e.g., MLflow, SageMaker, or Kubeflow). You’ve also initiated the implementation of the remaining features, ensuring the infrastructure supports scalable, repeatable workflows for model experimentation and deployment in both R&D and production environments.

Required Skills & Abilities

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.

Strong knowledge of ML lifecycle: data ingestion, model training, evaluation, packaging, and deployment.

Hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Airflow, Metaflow, or similar).

Deep understanding of CI/CD practices applied to ML workflows.

Proficiency in Python, Git, and system design with solid software engineering fundamentals.

Experience with cloud platforms (AWS, GCP, or Azure) and designing ML architectures in those environments.

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.

We are committed to providing fair and transparent compensation in accordance with applicable laws. Salary ranges are listed below and reflect the expected range for new hires in this role, based on factors such as skills, experience, qualifications, and location. Final compensation may vary and will be determined during the interview process. The target hiring range for this position is listed below.

Target Salary Range

$150,000—$250,000 USD

Parallel Systems is an

Qualifications

Experience:

5 years experience

Apply now

Please let Parallel know you found this job on Job Vista. This helps us grow!

Apply now

Job Overview

Company Parallel

Location Los Angeles, CA

Job Type Full Time

Salary USD 150,000 - 250,000

Experience 5 years experience

Posted 6 days ago

Deadline Jan 24, 2027

About Company

Industry Information Technology

Founded -

View Company Profile ⟶

Quick Actions