Senior Machine Learning Engineer

Senior Machine Learning Engineer

Iterable is the leading AI‑powered customer engagement platform that helps leading brands like Redfin, Seat Geek, Priceline, Calm, and Box create dynamic, individualized experiences  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—Trust, Growth Mindset, Balance, and Humility—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, New York, Denver, London, 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.

We’re looking for a talented Senior Machine Learning Engineer to join a cross‑functional machine learning team shaping the future of our platform’s AI capabilities. In this role, you’ll architect and develop robust systems for feature engineering and large‑scale model training—collaborating across teams, navigating complex data challenges, and guiding technical direction.

This is an exceptional opportunity for someone with a strong platform mindset who is passionate about building end‑to‑end ML workflows, enjoys tackling real‑world data problems, and thrives on ownership from ideation through to deployment. You’ll play a pivotal part in designing reusable, scalable ML infrastructure, enabling teams to accelerate experimentation and bring intelligent features to life. While specific domain expertise in certain ML methods is a plus, what matters most is your ability to identify impactful opportunities, prototype new solutions, and continuously advance our platform’s machine learning capabilities.

• Independently lead large‑scale machine learning initiatives—delivering capabilities for scalable feature engineering, data processing, and model training on Databricks.
• Design, build, and deploy machine learning models that enable our partners to reach the right user with the right message at the right time.
• Own the complete lifecycle of ML platform features: from requirements gathering and architecture, through implementation, deployment, and post‑launch support.
• Shape architectural decisions aimed at building robust, reusable, and highly available ML infrastructure that raises the bar for engineering and data science excellence.
• Mentor colleagues through code reviews, technical design sessions, and knowledge sharing, helping grow a strong culture of engineering rigor and learning.

• Have 5+ years of experience in machine learning engineering, data infrastructure, or platform engineering, preferably in a SaaS environment.
• Demonstrate a strong track record leading multi‑stakeholder projects that deliver platform features, scalable ML tooling, or end‑to‑end training systems.
• Show proficiency with Python (with a preference for experience in distributed data processing environments like Databricks, Spark, or similar platforms).
• Bring hands‑on experience with large‑scale data pipelines, distributed systems, and cloud data storage (Databricks Delta, Spark, Kafka, Postgres, etc.).
• Exhibit a product‑minded approach: comfortable partnering with product managers and data practitioners to balance trade‑offs across…