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