About this role
Optimove is developing Positionless Marketing, an AI-powered platform designed to enable marketers to independently analyze, create, launch, and optimize campaigns. This platform aims to deliver faster execution, deeper personalization, and increased campaign efficiency for users. The company partners with prominent brands such as Sephora and Staples, indicating a focus on high-impact applications within the marketing technology space. The culture emphasizes ownership, growth, and internal promotion, suggesting an environment where engineers are empowered to drive product features from inception to deployment.
The Machine Learning Engineer will join the Personalize team, focusing on building the products that allow customers to personalize messages across all digital touchpoints. This role involves working extensively with text data and leveraging advanced technologies like Large Language Models (LLMs) to bring accessible intelligence to the platform. The position requires an engineer capable of owning meaningful components of the personalization roadmap entirely, from initial problem framing through to final deployment and ongoing monitoring with minimal supervision.
This role is suited for an engineer who thrives on end-to-end ownership and enjoys contributing across the full spectrum of machine learning and software engineering, including early-stage research and production support. Individuals who are proactive in resolving technical challenges and eager to experiment rapidly to ship solutions quickly would find this environment highly suitable.
What you will do
- Own the complete delivery of medium-sized ML features within Personalize, covering problem framing, data preparation, model building, training, evaluation, deployment, and monitoring.
- Develop predictive ML models focused on classification, ranking, and personalization, utilizing text data effectively.
- Apply Large Language Models and other advanced techniques to enhance the product's capabilities.
- Operationalize developed models as APIs, supporting both real-time and batch processing environments.
- Monitor deployed models to prioritize data quality issues and model degradation.
- Research new Machine Learning applications and improve existing models, sharing findings across the broader engineering team.
- Collaborate with product, MLOps, and engineering teams to define and prepare new ML applications for development.
What you need
- A Bachelor's degree or equivalent professional experience in Computer Science, Data Science, Statistics, or a related field is required.
- Candidates must have 1 to 4 years of professional experience building and deploying ML models.
- A strong grasp of core ML concepts, including supervised/unsupervised learning, model evaluation, and feature engineering, is essential.
- Proficiency in Python, including libraries such as pandas and NumPy, along with strong SQL skills is necessary.
- Familiarity with the standard ML toolkit, including Git, Docker, scikit-learn, PyTorch or TensorFlow, and ML pipelines is required.
- Hands-on experience working with text data and relevant Natural Language Processing techniques is mandatory.
- Solid experience with cloud technologies and data storage solutions, specifically including Snowflake, is required.
- A basic understanding of responsible AI practices, such as data privacy, bias/fairness, and explainability, is needed.
Nice to have
- Understanding of personalization across domains such as sports betting and gaming and best practices.
- Full understanding of recommendation algorithms.
Benefits
- Two out of every three managers are promoted from within.
Skills and tech stack
Questions candidates ask
What is the remote work policy?
This is a Hybrid role, and you will be required in the Dundee office two days per week.
What is the compensation structure?
The posting does not specify the salary for this role.
What is the primary tech stack used?
The technology stack includes LLMs, Python, pandas, NumPy, SQL, Git, Docker, scikit-learn, PyTorch or TensorFlow, and Snowflake.
How is the team structure?
The team is dynamic and spans ML, MLOps, and software engineering, fostering a collaborative environment.