The opportunity
Iterable is the AI customer engagement platform, built for enterprise scale, loved by teams, and trusted by global brands like Redfin, SeatGeek, Priceline, Calm, and Box. Our platform empowers organizations to activate customer data from any source, design seamless cross-channel…
What you'll do
Build and ship core product ML capabilities, including send-time/frequency: optimization, recommendation engines, and campaign personalization.
Own the full model lifecycle end-to-end: EDA, feature engineering, distributed training, production deployment, latency monitoring, and online experimentation (e.g., multi-armed bandits, ensemble models).
Build and scale our data pipelines and serving architectures using: Databricks, Spark, AWS, Ray, and Kubernetes.
Partner with Backend and Platform teams to elevate feature serving, improve: overall data architecture, and establish engineering standards across the team.
Mentor engineers on the team and help set the technical direction for ML at Iterable.
What they're looking for
- + years of hands-on MLE experience putting complex models into production at scale.
- Databricks & Spark mastery: Real experience building, optimizing, and scaling production data pipelines using Databricks.
- Production-level Model Engineering: Proven track record of taking deep learning and ensemble architectures from training through optimization to production deployment.
- Production Code: Strong Python and/or Scala skills with an emphasis on readable, modular, and maintainable systems code.
- Systems & Infra Experience: Hands-on work with microservice backends, distributed compute (Ray/Spark), and containers (Kubernetes).
- Pragmatism: You care more about serving latency, reliability, and measurable: customer impact than novel theoretical architectures.
