Stord is The Consumer Experience Company, powering seamless checkout through delivery for today's leading brands. Stord is rapidly growing and is on track to double our revenue in the next 18 months. To meet and exceed this target, Stord is strategically scaling teams across the entire company, and seeking energetic experts to help us achieve our mission.

By combining comprehensive commerce-enablement technology with high-volume fulfillment services, Stord provides brands a platform to compete with retail giants. Stord manages over $10 billion of commerce annually through its fulfillment, warehousing, transportation, and operator-built software suite including OMS, Pre- and Post-Purchase, and WMS platforms. Stord is leveling the playing field for all brands to deliver the best consumer experience at scale.

With Stord, brands can increase cart conversion, improve unit economics, and drive sustained customer loyalty. Stord’s end-to-end commerce solutions combine best-in-class omnichannel fulfillment and shipping with leading technology to ensure fast shipping, reliable delivery promises, easy access to more channels, and improved margins on every order.

Hundreds of leading DTC and B2B companies like AG1, True Classic, Native, Seed Health, quip, goodr, Sundays for Dogs, and more trust Stord to deliver industry-leading consumer experiences on every order. Stord is headquartered in Atlanta with facilities across the United States, Canada, and Europe. Stord is backed by top-tier investors including Kleiner Perkins, Franklin Templeton, Founders Fund, Strike Capital, Baillie Gifford, and Salesforce Ventures.

About the Role:

Stord is building ML capabilities that directly power our cloud-based supply chain platform, which handles over $10B in commerce annually. You'll work alongside a Senior Data Scientist to own the full ML lifecycle — from model design and training through production deployment and ongoing improvement. This is a high-impact, hands-on role on a small team where your work will directly influence how millions of shipments are planned, routed, and fulfilled.

As a Machine Learning Engineer at Stord, you 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. You'll inherit an existing production model and expand our ML capabilities across core logistics use cases including delivery time estimation, demand forecasting, and operational intelligence. This is a rare opportunity to shape ML practices from the ground up on a small, ambitious team with direct visibility into customer impact.

What You'll Do

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

What You'll Need

Required

• 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

Bonus

• 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