Senior MLOps Engineer (Machine Learning Infrastructure)

Location: Hybrid in Los Angeles, California, with at least 1 week per month onsite

Salary: $150,000–$250,000 USD per year

Company

hirify.global develops autonomous battery-electric rail vehicles to create cleaner, safer, and more efficient freight transportation.

What you will do

• Design and implement MLOps solutions for data management, model training, deployment, monitoring, evaluation, and selection.
• Architect and operate scalable infrastructure for distributed ML training and inference.
• Build cloud-based ML systems for R&D and production environments using platforms such as AWS and GCP.
• Develop infrastructure supporting CI/CD, experiment management, and governance of models and datasets.
• Collaborate with ML engineers and stakeholders to define requirements, architecture, and deployment strategies.
• Integrate tools such as MLflow, SageMaker, or Kubeflow and deliver repeatable ML workflows.

Requirements

• Bachelor’s or higher degree in Computer Science, Machine Learning, or a relevant engineering discipline.
• 5+ years of experience building large-scale reliable systems, including 2+ years focused on ML infrastructure or MLOps.
• Experience architecting and deploying production-grade ML pipelines and platforms.
• Strong understanding of the ML lifecycle, including data ingestion, training, evaluation, packaging, and deployment.
• Hands-on experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Airflow, or Metaflow.
• Proficiency in Python, Git, system design, CI/CD, and cloud ML architectures across AWS, GCP, or Azure.

Nice to have

• Experience with deep learning architectures, computer vision, or perception systems.
• Experience with distributed training tools such as PyTorch DDP, Horovod, or Ray.
• Background in real-time ML systems, batch inference, and CPU/GPU-aware orchestration.
• Previous experience in autonomous vehicles, robotics, or other real-time ML-driven systems.

Culture & Benefits

• Work on autonomous battery-electric rail vehicles and safety-critical ML systems.
• Collaborate with engineers across autonomy, robotics, software, and machine learning.
• Inclusive workplace committed to equal opportunity and reasonable accommodations.
• Compensation is determined based on skills, experience, qualifications, and location.