Lead Software Engineer - Machine Learning Platform

What you'll do

• Design, build, and maintain end-to-end ML training platforms and infrastructure, including Kubernetes-based workloads.
• Optimize single-node and distributed GPU training for performance, cost, utilization, and reproducibility.
• Enable governed Gen AI/LLM training workflows and improve observability, developer tooling, and team adoption of validated AI-assisted engineering practices.

What they ask for

• Formal training or certification in software engineering concepts and 5+ years of applied experience.
• Experience running cloud ML training and debugging infrastructure and code; Python skills and ML-codebase automation/CI experience.
• Hands-on deep-learning training experience with PyTorch or TensorFlow, including distributed training and performance optimization.
• Experience with Kubernetes and AWS, and leading safe, validated use of approved AI-assisted software development tools.

Nice to have

• Experience with multi-cloud training, cloud-native networking/storage, large-dataset input pipelines, or distributed compute frameworks such as Spark, Ray, or Dask.
• Familiarity with workflow orchestration, model registries, training cost optimization, and training-system observability.

Pay

USD 156,750.00-$215,000.00 base salary (Palo Alto, CA); USD 156,750.00-$215,000.00 base salary (Jersey City, NJ); USD 156,750.00-$215,000.00 base salary (Seattle, WA); USD 156,750.00-$215,000.00 base salary (New York, NY)

Tools

Python, PyTorch, TensorFlow, Kubernetes, EKS, AWS, Spark, Ray

AI focus

Enables Gen AI/LLM training and fine-tuning workflows, including supervised fine-tuning and evaluation. Also leads adoption of enterprise-authorized AI-assisted engineering tools, with validation for correctness, performance, and security.