Senior Machine Learning Engineer (Nova)

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 engagementall with enterprise-grade security and compliance. Today nearly 1200 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 valuesBe 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. Thats why weve been recognized as one of Incs Best Workplaces and Fastest Growing Companies and were recognized on Forbes list of Americas Best Startup Employers in 2022. Notably Iterable has also been listed on Wealthfronts Career Launching Companies List and has held a top 10 ranking on the Top 25 Companies Where Women Want to Work.

With a global presenceincluding offices in San Francisco Denver London Sydney and Lisbon plus remote employees worldwidewe 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. Lets 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 Novas 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.

• 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.
  
  

• 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.

• Experience building ML or LLM platforms tooling or developer-facing frameworks.
• Prior work with embeddings searchranking 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.

• Competitive salaries & meaningful equity
• Private Medical Insurance
• Life/Risk Assurance
• Meal Allowance: 8.55 per day
• Community Days (days for us to give back to the community)
• Paid Annual Leave (22 days)
• Global Lifestyle Reimbursement Account
• Paid Sabbatical
• Complete laptop workstation

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