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Iterable is seeking a Senior Machine Learning Engineer to build core ML foundations for Nova, an AI-powered customer engagement platform. The role focuses on applied ML in production environments: designing retrieval systems, evaluation frameworks, and model integration layers that power agentic experiences.
Key responsibilities include:
- Design and build ML platform components supporting agentic systems, including retrieval pipelines, indexing strategies, and model integration layers
- Operationalize RAG use cases from data sourcing through runtime retrieval patterns
- Develop generalized evaluation frameworks for LLM and agent-based features with offline metrics, golden datasets, and continuous monitoring
- Implement abstractions, tooling, and reusable patterns enabling other teams to build ML/LLM-powered experiences
- Partner with backend engineers to productionize ML features with strong reliability, observability, and performance
- Prototype applied ML solutions to validate feasibility before full builds
- Ensure secure, robust handling of data in ML workflows and retrieval operations
- Collaborate with product, design, and engineering teams to align ML system design with user experience and product goals
- Contribute to iterative improvements of the Nova agent framework using Mastra and TypeScript
Required qualifications:
- 5+ years as a Machine Learning Engineer or similar role focused on production systems
- Strong engineering skills in Python or TypeScript, including ML workflow 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 ambiguous areas
- Strong communication and collaboration skills in distributed environments
Bonus experience includes ML/LLM platform building, embeddings and search-ranking systems, event-driven or streaming architectures, model observability and monitoring, and personalization backgrounds.
