Senior Machine Learning Engineer, Nova Intelligence

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 experiences, and optimize engagement with Nova Intelligence, the AI layer of our platform, all with enterprise-grade security and compliance. Today, nearly 1,200 brands across 50+ countries rely on Iterable to drive growth, deepen customer relationships, and deliver joyful customer experiences.

Our success is powered by extraordinary people who bring our core values to life every day: Be an Owner, Growth Mindset, Run as One, and Transparency. And the market has noticed: G2 recognizes Iterable as a Leader globally, spanning the Americas, EMEA, APAC, ANZ, and Latin America, with category leadership in Personalization, Marketing Automation, Push Notifications, and Mobile Marketing, and a 4.5/5-star rating from hundreds of users. Gartner named Iterable a Challenger in the Gartner Magic Quadrant for Multichannel Marketing in both 2024 and 2025. We’ve also earned TrustRadius Top Rated honors in 2024 and 2025, were named a TrustRadius Buyer’s Choice for 2026, and were featured in Snowflake’s Modern Marketing Data Stack 2026.

With a global presence that includes offices in San Francisco, Denver, London, Sydney, and Lisbon, plus remote employees worldwide, we are committed to building a diverse and inclusive workplace. We welcome candidates from all backgrounds and encourage you to apply. Learn more about our story and mission on our Culture and About Us pages. Let’s shape the future of customer engagement together!

Location: US

As a Senior Machine Learning Engineer at Iterable, you will design and build the end-to-end machine learning systems that power intelligence across our product. As a high-tech marketing platform, we work with thousands of large-scale and diverse enterprise customers. The scale and diversity of these workloads — supporting personalization with deep customizability — present unique and exciting challenges for machine learning at production scale.

You will have real autonomy: from research and experimentation through to models running reliably in production, you'll own the systems and models behind our ML products and help shape the direction of AI at Iterable.

One of our core values is a growth mindset, and Iterable is a company where everyone can grow. If this role excites you, please apply — we value applicants for the skills they bring beyond a job description.

• 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.

• 5+ 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.

• Prior work on large-scale recommendation systems.

• Experience setting up feature stores or model observability tools.

• Exposure to Infrastructure-as-Code (Terraform, Pulumi).

• Building, evaluating, and integrating new models that add intelligence into our core product.

• Enhancing models that optimize send time and frequency of outgoing message campaigns.

• Designing data pipelines that make it easy to implement and test new models.

• Working with platform engineers to enhance our feature- and model-serving architecture.

• Benchmarking and improving our multi-armed bandit implementation.

• Building tools to visualize and debug features and models.

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