Principal ML Ops Engineer

📋 Description

• Architect and scale the end-to-end ML Ops pipeline (training, fine-tuning, eval, rollout, monitoring).
• Design deployment infrastructure with versioning, reproducibility, and orchestration across cloud and on-prem GPUs.
• Optimize compute usage across Kubernetes, autoscaling, caching, and GPU allocation.
• Lead observability for ML systems: drift, performance, throughput, reliability, cost.
• Build automated data workflows: curation, labeling, features, evaluation, and ML CI/CD.
• Collaborate with researchers to productionize models and accelerate training/inference.

🎯 Requirements

• Deep hands-on experience designing and operating production ML systems at scale (Staff/Principal-level).
• Strong background in ML Ops, distributed systems, and cloud infrastructure (AWS, GCP, or Azure).
• Proficiency with Python and familiarity with TypeScript or Go for platform integration.
• Expertise in ML frameworks: PyTorch, Transformers, vLLM, Llama-factory, Megatron-LM, CUDA / GPU acceleration.
• Strong experience with containerization and orchestration (Docker, Kubernetes, Helm, autoscaling).
• Deep understanding of ML lifecycle workflows: training, fine-tuning, evaluation, inference, model registries.

🎁 Benefits

• Competitive salary & equity options
• Sign-on bonus
• Health, Dental, and Vision
• 401k