Design, train, and evaluate ML models for logistics use cases: delivery time estimation, demand forecasting, capacity planning, anomaly detection
Improve and iterate on existing production models using performance data and customer feedback
Run structured experiments to validate model improvements before promotion to production
Own the full path from trained model to production API — wrapping, deploying, versioning, and monitoring
Build and maintain inference APIs serving predictions at scale (<100ms latency targets)
Deploy and manage models on GCP Vertex AI
Implement A/B testing and rollback strategies for safe model promotion
Build real-time and batch feature pipelines from Postgres/AlloyDB sources
Design feature stores serving both training and inference
Implement data validation and quality monitoring to catch drift before it affects customers
Develop CI/CD pipelines for model deployment
Monitor model and pipeline health; own incident response for ML systems
Optimize inference costs across GCP and Cloudflare infrastructure
Partner with the Data Scientist on experiment design and feature strategy
Work with platform engineers to integrate ML outputs into core product services
Communicate model behavior, limitations, and tradeoffs clearly to non-ML engineers and product stakeholders

4+ years of ML engineering experience, with models shipped to production
Strong Python — training pipelines, model evaluation, production code (not just notebooks)
Experience with cloud ML platforms, preferably GCP Vertex AI
Data engineering fundamentals: ETL/ ELT , streaming data, SQL at scale
TypeScript or Elixir experience, or demonstrated ability to build APIs in unfamiliar languages
Familiarity with logistics, e-commerce, fulfillment, or supply chain domains — you understand what on-time delivery, carrier selection, and warehouse throughput actually mean operationally

Senior Machine Learning Engineer

Key skills