Machine Learning Engineer

At Optimove, we believe people are capable of more than a single job description. You’re not hired just to fill a position- you’re empowered to shape it, grow it, and make it your own. We call this being Positionless. And Positionless isn’t just our culture. It’s our product. Optimove is the creator of Positionless Marketing, an AI-powered platform that gives every marketer the power to analyze, create, launch, and optimize independently. The result is faster execution, deeper personalization, and 88% greater campaign efficiency. Recognized as a Visionary in Gartner’s Magic Quadrant, we partner with leading brands like Sephora, Staples, and Entain. Today, more than 500 Optimovers across NYC, London, Tel Aviv, Scotland, Brazil, Estonia, and beyond are building the future of marketing together, in an environment that actively encourages ownership and growth, with two out of every three managers promoted from within. If you’re looking for a place where you can do more, be more, come grow with us. About the Role As a Machine Learning Engineer, you'll join our Personalize team, helping shape and build the products that let our customers personalise messages across every digital touchpoint. You'll work with text data and with cutting-edge technologies including Large Language Models (LLMs), bringing Accessible Intelligence to our customers across both Personalize and Optimove's overall platforms. This is a role for an engineer who's ready to own meaningful, medium-sized pieces of our personalisation roadmap end-to-end - from problem framing through to deployment and monitoring - and trusted to do so with minimal oversight. It's not solo delivery: you'll be working closely with a dynamic team spanning ML, MLOps and software engineering, and should be happy to contribute at every level, from early-stage research through to production support. Role & Core Responsibilities Own the delivery of medium-sized ML features end-to-end within Personalize - problem framing, data preparation, model build/train, evaluation, deployment and monitoring - to predictable timelines. Develop predictive ML models for classification, ranking and personalisation, working with our text data. Leverage LLMs and other state-of-the-art techniques to enhance product capabilities. Operationalise models as APIs across real-time and batch environments. Monitor production models in your own scope, treating data quality issues and model degradation as a priority. Research new ML applications and improve pre-existing models, sharing findings with the wider ML, MLOps and engineering team. Collaborate closely with product, MLOps and engineering teams to define and prepare new ML applications, contributing meaningfully to planning and grooming. Proactively surface and resolve technical and data challenges before they affect delivery, model quality or customers. Best Bits of the Job Exposure to a wide range of ML domains, including large-scale search, ranking, Natural Language Processing, hybridisation, classification and text data processing. Working with modern ML technologies, including LLMs, to enhance our products. Fully real-time architecture for data processing, model development and deployment. Deploying and enhancing ML frameworks, optimising for inference and training/retraining cycles. Online testing of models with live data, using our proprietary A/B/N testing technology to see quickly what performs well. A supportive, collaborative team spanning ML, MLOps and software engineering, where rapid experimentation is the norm. Dedicated time to research new methods, build proofs-of-concept, and ship to production quickly when they work. Everyday use of modern AI coding assistants (e.g. Claude) to speed up experimentation and review. Essential Requirements Bachelor's degree (or equivalent) in Computer Science, Data Science, Statistics or a related field, OR 1–4 years' professional experience building and