Senior Machine Learning Engineer

Stord is The Consumer Experience Company, powering seamless checkout through delivery for today's leading brands. The Senior Machine Learning Engineer will own the end-to-end delivery of ML features, designing and training models, deploying them to production, and ensuring they perform reliably at scale.

Responsibilities:

• Model Development 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
• Define evaluation frameworks and success metrics in collaboration with the Data Scientist and product teams
• Productionization & Deployment- 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
• Data Pipelines & Feature Engineering 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
• Infrastructure & Reliability 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
• Collaboration 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

Requirements:

• 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
• Kafka or streaming pipeline experience
• Feature store experience (Feast, Tecton, or equivalent)
• Hands-on with Kubernetes or container orchestration
• Cloudflare Workers or edge inference experience
• Experience improving existing production models, not just building greenfield