Senior Machine Learning Engineer, Agentic Evals and Self-Improvement (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

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 evaluation and automatic optimization for Nova's models and agents. That means measuring how they perform, finding where they fail, and using those findings to automatically improve prompts, context, tools, and agent harnesses. You'll work with the Nova retrieval and product teams to connect real interactions to measurable improvements.

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.

• Build evaluation systems for models and agents: benchmarks, LLM judges, simulations, trajectory analysis, and regression tests.

• Create a loop that takes failures from diagnosis to fix to measured improvement.

• Turn evaluation results into automatic optimization of prompts, context, tools, rubrics, and agent harnesses.

• Build synthetic data pipelines for evaluation and post-training, with attention to quality, privacy, and reproducibility.

• Apply recent research (LLM-as-judge, reward modeling, test-time search, post-training) to production.

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

• 5+ years of hands-on MLE experience putting complex ML systems into production at scale, such as production ML pipelines or large-scale experimentation platforms.

• 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.

• Strong Python skills with an emphasis on readable, modular, and maintainable code, plus experience with modern ML frameworks.

• Deep experience evaluating LLMs or agents, including multi-turn behavior, tool use, and reasoning.

• Experience building automated evaluation, such as LLM judges, reward models, or benchmark infrastructure.

• Experience with at least one of: prompt or context optimization, post-training, preference optimization, or reinforcement learning.

• Strong communication and cross-team collaboration.

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

• Experience with automatic prompt optimization and error analysis

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