Senior Machine Learning Engineer (Nova)

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! We are looking for a Senior Machine Learning Engineer to build the core Machine Learning foundations that power Nova’s agentic experiences. This role focuses on applied Machine Learning in production environments: retrieval systems, evaluation frameworks, and model integration layers that make AI features reliable, scalable, and repeatable. You will design and implement the underlying components that support rich, intelligent interactions in the Iterable platform. You will work closely with backend, frontend, and product teams to shape how Machine Learning is introduced and maintained across the company. The work blends hands-on engineering with system design, and is ideal for someone who can drive complex efforts independently, make practical architectural decisions, and collaborate in a fast-moving, cross-functional product environment. Position Details: Design and build Machine Learning platform components that support agentic systems, including retrieval pipelines, indexing strategies, and model integration layers. Introduce and operationalize RAG use cases, from data sourcing and embedding generation to runtime retrieval patterns. Develop generalized evaluation frameworks for LLM- and agent-based features, including offline metrics, golden datasets, and continuous monitoring. Implement abstractions, tooling, and reusable patterns that enable other teams to build ML- and LLM-powered experiences efficiently. Partner with backend engineers to productionize ML features with strong reliability, observability, and performance characteristics. Prototype applied ML solutions to validate feasibility before investing in full builds. Ensure secure, robust handling of data used in ML workflows and retrieval operations. Collaborate with product, design, and engineering to align ML system design with user experience and product goals. Contribute to iterative improvements of the Nova agent framework, including workflows built with Mastra and TypeScript. The Ideal Candidate Will Be/Have: 5+ years of experience as a Machine Learning Engineer or similar role focused on production systems. Strong engineering skills with Python or TypeScript, including experience building ML workflows in frameworks like Mastra or comparable agent/LLM toolkits. Experience with retrieval systems, vector databases, search technologies, or RAG architectures. Prior work integrating ML or LLM-powered features into production applications. Understanding of ML evaluation techniques, experimentation design, and failure analysis. Ability to lead complex projects, make practical trade-offs, and work independently in areas of ambiguity. Strong communication and collaboration skills in a distributed environment. Bonus Points: Experience building ML or LLM platforms, tooling, or developer-facing frameworks. Prior work with embeddings, search–ranking systems, or advanced RAG architectures. Familiarity with event-driven systems or streaming architectures. Experience with model observability, performance monitoring, or proactive regression detection. Background in personalization, recommendations, or applied NLP. Experience working in remote-first engineering teams. What we offer  Competitive salaries & meaningful equi