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Senior ML Ops Engineer (Machine Learning Infrastructure)
Parallel Systems
Lead design and development of scalable MLOps infrastructure for autonomous rail vehicles.
7d ago
$150,000 - $240,000
Expert & Leadership (13+ years), Experienced (8-12 years)
Full Time
Los Angeles, CA
Hybrid
Company Size
45 Employees
Service Specialisms
No specialisms available
Sector Specialisms
Railroads
Freight
Autonomous Rail
Battery-Electric Rail Vehicles
Logistics
Supply Chain
Ports
Transportation Infrastructure
Role
What you would be doing
mlops pipeline
ml infrastructure
cloud systems
workflow automation
ci/cd
monitoring
- 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.
- Architect, deploy, and manage scalable ML infrastructure for distributed training and inference.
- Support the automation of model evaluation, selection, and deployment workflows.
- 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.
- Design and implement robust MLOps solutions, including automated pipelines for data management, model training, deployment and monitoring.
- Collaborate with ML engineers to gather requirements and develop strategies for data management, model development and deployment.
What you bring
ci/cd
mlops
distributed training
python
5+ years
bachelor's
- Deep understanding of CI/CD practices applied to ML workflows.
- Hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Airflow, Metaflow, or similar).
- Hands-on experience with distributed training tools (e.g., PyTorch DDP, Horovod, Ray).
- 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.
- Experience with cloud platforms (AWS, GCP, or Azure) and designing ML architectures in those environments.
- Bachelor’s or higher degree in Computer Science, Machine Learning, or a relevant engineering discipline.
- Proven experience architecting and deploying production-grade ML pipelines and platforms.
- Background in real-time ML systems and batch inference, including CPU/GPU-aware orchestration.
- 5+ years of experience building large-scale, reliable systems; 2+ years focused on ML infrastructure or MLOps.
- Proficiency in Python, Git, and system design with solid software engineering fundamentals.
- Experience with deep learning architectures (CNNs, RNNs, Transformers) or computer vision.
- Strong knowledge of ML lifecycle: data ingestion, model training, evaluation, packaging, and deployment.
- Previous work in autonomous vehicles, robotics, or other real-time ML-driven systems.
Information not given or found
Training + DevelopmentInformation not given or found
Interview process
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Company
Overview
- Operates with a vision to revolutionize freight transportation using autonomy and sustainability.
- Focuses on developing electric, autonomous rail systems to reduce carbon emissions and improve efficiency.
- Has developed a cutting-edge platform for rail freight that minimizes human involvement and reduces operational costs.
- Has secured partnerships with logistics industry leaders to implement their autonomous systems for expansive use.
- Serves as a leader in the electrification of the rail sector through their innovative approach to freight transport.
- Their technology sets the stage for the future of autonomous rail logistics, streamlining transportation networks.
- Collaborative
- Autonomous
- Entrepreneurial
- Fast-paced
- People-first
- Progressive
- Supportive
- Mission-driven
- Data-driven
- Trust
- Originality
- Ownership
25% energy
Energy Efficiency
Autonomous battery-electric freight rail vehicles use just 25% of the energy compared to a semi-truck.
~33B gallons
Diesel Offset
Tech offsets approximately 33 billion gallons of diesel annually.
58,000 kg
Payload Capacity
Prototype can carry a payload of 58,000 kg with an 800 km range.
160-mile corridor
Pilot Corridor
Piloting zero-emissions railcars approved by FRA on a 160-mile Georgia corridor.
- Awarded $4.43M ARPA-E grant (Nov 3 2022–May 2 2025) to test energy performance and environmental impact.
- Tech offsets ~4.5% of all U.S. GHG emissions annually.
- DOE partnership includes analysis with NREL and RailTEC on energy efficiency.
- Piloting zero-emissions railcars aiming for commercialization by 2026.
- Founded and staffed by professionals with backgrounds at SpaceX, Tesla, Google and Uber
- Team described as diverse mission-driven professionals, fostering a collaborative culture of inclusion, trust and originality
- No publicly disclosed gender breakdown or specific gender-related statistics available
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