Senior Machine Learning Engineer (Nova)

About the role

This Senior Machine Learning Engineer position at Iterable focuses on building the core machine learning infrastructure for Nova's agentic features. The work involves applied machine learning in production settings, specifically designing and implementing retrieval systems, creating evaluation frameworks, and developing model integration layers. The goal is to ensure that AI features within the Iterable platform are reliable, scalable, and repeatable for users.

A key part of this role is operationalizing Retrieval Augmented Generation (RAG) use cases, from sourcing data and generating embeddings to managing runtime retrieval patterns. The engineer will also develop generalized evaluation frameworks for LLM and agent-based features, including defining offline metrics, creating golden datasets, and setting up continuous monitoring. This work directly supports the creation of intelligent interactions within the platform.

Success in this position means enabling other teams to efficiently build ML and LLM-powered experiences by providing robust abstractions, tooling, and reusable patterns. The role requires partnering with backend engineers to ensure ML features are production-ready with high reliability, observability, and performance. It also involves prototyping applied ML solutions to validate their feasibility before full-scale development.

The salary for this role ranges from $140,000 to $200,000 USD.

Skills that matter here

• Python: This role requires strong engineering skills in Python for building ML workflows and production systems.
• TypeScript: Strong engineering skills in TypeScript are needed, particularly for contributing to workflows built with the Nova agent framework.
• Machine Learning: The position is centered on applied machine learning, focusing on building core ML foundations and integrating ML features into production applications.
• LLM: Experience with LLM-powered features, including evaluation techniques and integration into applications, is a key requirement.
• Mastra: Experience with Mastra or comparable agent/LLM toolkits is required for building ML workflows.

Who this role suits

• You have at least five years of experience in a production-focused Machine Learning Engineer role.
• You are adept at leading complex projects and making practical trade-offs, even in ambiguous situations.
• You are comfortable working independently in a distributed environment and possess strong communication skills.
• You have a solid understanding of ML evaluation techniques, experimentation design, and failure analysis.

From the employer

• 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 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.

• Competitive salaries, meaningful equity, & 401(k) plan
• Medical, dental, vision, & life insurance
• Balance Days (additional paid holidays)
• Fertility & Adoption Assistance
• Paid Sabbatical
• Flexible PTO
• Monthly Employee Wellness allowance
• Monthly Professional Development allowance
• Pre-tax commuter benefits
• Complete laptop workstation