Senior Machine Learning Engineer, Agentic Context Retrieval and Search (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!

Location: US

About The Role

As a high-tech marketing platform, Iterable works with thousands of large-scale and diverse enterprise customers. Iterable Nova is our intelligence layer. It helps customer experience teams adapt in real time and improve every interaction.

As a Senior Machine Learning Engineer on Nova, you'll lead context retrieval and search. The goal is agent responses that are grounded in evidence, aware of the customer's context, and based on what a query means rather than its literal words. You will build the retrieval stack and the search subagents that run on top of it.

You will have real autonomy: from research and experimentation through to systems running reliably in production, you'll own this work and help shape the direction of AI at Iterable.

One of our core values is a growth mindset, and Iterable is a company where everyone can grow. If this role excites you, please apply — we value applicants for the skills they bring beyond a job description.

What You'll Do

Build retrieval across structured data (tables, SQL, dashboards, code) and unstructured content (docs, tickets, chat, media), using each source's own signals.

Build semantic query understanding for agents: query rewriting, decomposition, intent classification, and entity resolution for multi-turn workflows.

Build pipelines that extract entities, embeddings, summaries, and metadata from customer data, and keep them fresh.

Build search subagents that decide what to retrieve, run multi-hop searches, check whether results are good enough, and flag when retrieval fails.

Measure quality with ranking metrics (nDCG, MRR, Recall@K), LLM judges, human labels, and online tests. Track how retrieval affects final responses, and use the results to improve both retrieval and the harness.

Mentor engineers on the team and help set the technical direction for Nova.

What We're Looking For

5+ years of hands-on MLE experience putting complex models into production at scale, including search, information retrieval, or recommendation systems.

Master's or PhD in Computer Science, Machine Learning, AI, or a related field.

You keep up with the latest research and focus on applying it: shipping reliable changes that measurably improve the product experience for customers.

Deep IR expertise: lexical (BM25), dense (embeddings, ANN indexes), and hybrid retrieval, plus learning-to-rank.

Production experience with search engines such as Elasticsearch, OpenSearch, or Vespa, including relevance tuning and reranking.

Hands-on experience with RAG, query rewriting, reranking, and grounding techniques that reduce hallucination.

Experience building agents on top of retrieval, such as multi-hop search or sufficiency checks.

Strong evaluation practice: offline benchmarks, regression detection, A/B testing, and labeling pipelines.

Bonus points

Experience with LLM APIs from multiple providers (Anthropic, OpenAI, Google), including tool calling and prompt caching.

Experience indexing and ranking structured and unstructured data in the same system.

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Compensation R