MLOps Engineer

You will optimize, deploy, and operate large-scale machine-learning models. You will improve training and inference performance, reduce memory and compute overhead, scale workloads across distributed multi-GPU and cloud environments, and harden production systems for reliable, high-volume use.

Responsibilities

• Optimize, deploy, and operate large-scale machine-learning models
• Improve training and inference performance
• Reduce memory and compute overhead
• Scale workloads across distributed multi-GPU and cloud environments
• Profile models to improve throughput and latency
• Harden systems for long-running and high-volume workloads
• Translate research models into production-ready services

Requirements

• 5+ years of industry experience
• Experience deploying and operating machine-learning models in production
• Experience optimizing training and inference workloads
• Experience with distributed frameworks and tooling
• Experience with PyTorch and the scientific Python ecosystem
• Understanding of MLOps practices including experiment tracking, model versioning, reproducibility, and CI/CD
• Software engineering experience building reliable, well-tested, maintainable ML infrastructure
• Ability to collaborate with ML researchers

Benefits

• Substantial equity ownership