Related skills
python kubernetes gcp tensorflow pytorch📋 Description
- Design end-to-end MLOps patterns to boost ML development velocity
- Build a graph ML platform enabling scalable model iterations
- Tune performance: training time, efficiency, and GPU costs
- Optimize batch data processing with Beam, Spark, Ray Data
- Architect pipelines for billions of graph nodes and edges
🎯 Requirements
- 5+ years in ML infrastructure incl training and deployments
- Hands-on ML optimization: memory and GPU profiling
- Cloud ML platforms: GCP BigQuery, Google Cloud Storage, Terraform
- Experience with MLOps tools: MLflow or Wandb
- Proficiency in Python, PyTorch, TensorFlow
- Distributed training: Ray and Kubernetes
- Graph databases: Neo4j, JanusGraph, TigerGraph
🎁 Benefits
- Equity
- 401(k) with employer match
- Medical, dental, and vision insurance
- Generous vacation and parental leave
- Accommodations for disabilities
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