Machine Learning Engineer

Optimove is a global marketing tech company, recognized as a Leader by Forrester and a Challenger by Gartner. We work with some of the world’s most exciting brands, such as Sephora, Staples, and Entain, who love our thought-provoking combination of art and science. With a strong product, a proven business, and the DNA of a vibrant, fast-growing startup, we’re on the cusp of our next growth spurt. It’s the perfect time to join our team of 500 thinkers and doers across NYC, LDN, TLV, and other locations, where 2 of every 3 managers were promoted from within. Growing your career with Optimove is basically guaranteed.

As a Machine Learning Engineer, you will be working within our Personalization team, helping to shape and drive the development of numerous products and initiatives that allow our customers to personalise messages across all digital touchpoints. This includes working with multi-modal data such as images, text, and more, leveraging cutting-edge technologies including Large Language Models (LLMs). This is an exciting opportunity at the forefront of machine learning, helping to bring Accessible Intelligence to our customers with great scope to make a key difference across both OptiX and Optimove’s overall platforms.

We are looking for an experienced Machine Learning Engineer to work on incredibly interesting projects as we take our personalization capabilities to the next level. You will focus on developing and advancing ML/AI across our platforms, researching and investigating new machine learning applications within the company, and improving pre-existing models.

Role & Core Responsibilities

• Own the model development and release process across all products and internal platforms, including both OptiX and Optimove.
• Manage the cloud-hosted modelling environment.
• Operationalize models as APIs working in real-time and batch environments.
• Monitor production models, ensuring data quality and model performance.
• Develop predictive machine learning models for classification, ranking, and personalization purposes, utilizing multi-modal data including images and text.
• Leverage LLMs and other cutting-edge technologies to enhance product capabilities.
• Research and investigate new machine learning applications within the company, and improve on pre-existing models.
• Collaborate closely with product and development teams to define and prepare new ML applications.
• Analyse performance and continuously improve scoring processes for hosted models.

Best Bits of the Job

• Exposure to a phenomenal array of machine learning domains, including massive-scale search, ranking, NLP, hybridization, classification, multi-modal data processing (images, text, etc.), and far beyond.
• Leveraging state-of-the-art technologies, including Large Language Models (LLMs), to enhance our products and services.
• Fully real-time architecture for data processing, model development, and deployment.
• Deploying and enhancing ML frameworks, optimizing for inference, and training/retraining cycles.
• Online testing for models with live data using proprietary A/B/N testing technology to rapidly determine what works (and what doesn’t).
• A super-bright, supportive, and friendly machine learning team to work with in an environment where rapid experimentation is the norm.
• Regular time allocated to research new methods, build and test proofs-of-concept, and deploy to production instantly if effective.
• GPU support to efficiently train deep learning models.

Minimum Requirements

• Minimum 3 years of experience in a similar role.
• Strong programming skills and a good understanding of software engineering principles and clean code practices.
• Expert-level knowledge of Python for machine learning and data manipulation (pandas, NumPy).
• Advanced experience with SQL for data querying and manipulation.
• Experience with Git, Bash, Docker, and machine learning pipelines.
• Experience with open-source machine learning libraries like scikit-learn, PyTorch, TensorFlow, and SciPy.
• Hands-on experience working with multi-modal data (images, text) and relevant ML techniques.
• Experience with cloud technologies and data storage solutions, including Snowflake.
• Understanding of personalization for various domains, including sports betting and gaming, where it might add value and what best practices look like.
• Full understanding of recommendation algorithms and their applications.
• Professional experience in personalization and/or predictive CRM, and micro-segmentation.
• Experience with CI/CD pipelines and Infrastructure as Code (IaC) tools (Terraform, Bicep, etc.).