Senior Machine Learning Engineer

About this role

Iterable is the leading AI-powered customer engagement platform that helps leading brands like Redfin, SeatGeek, Priceline, Calm, and Box create dynamic, individualized experiences at scale. Our platform empowers organizations to activate customer data, design seamless cross-channel interactions, and optimize engagement—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—Be an Owner, Growth Mindset, Run as One, Transparency —to life. We foster a culture of innovation, collaboration, and inclusion, where ideas are valued and individuals are empowered to do their best work. That’s why we’ve been recognized as one of Inc’s Best Workplaces and Fastest Growing Companies, and were recognized on Forbes’ list of America’s Best Startup Employers in 2022. Notably, Iterable has also been listed on Wealthfront’s Career Launching Companies List and has held a top 10 ranking on the Top 25 Companies Where Women Want to Work.

With a global presence—including 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!

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.

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 We're Looking For

• 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.

Bonus points:

• Prior work on large-scale recommendation systems.

• Experience setting up feature stores or model observability tools.

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

You Might Work On

• Building, evaluating, and integrating new models that add…