Machine Learning Engineer (UK)

About This Opportunity

Our talent team at Get A Job.ai is representing a confidential dating technology company seeking a Machine Learning Engineer to join their Matching & Recommendations team in London, UK. This role offers the opportunity to develop ML systems that power discovery and personalization experiences at scale, directly impacting how millions of users form meaningful connections.

As a Machine Learning Engineer, you'll take ownership of well-defined ML problems from initial exploration through production deployment and monitoring. You'll work with modern frameworks and collaborate across engineering, product, and data teams to deliver practical solutions that improve recommendation quality and user experience.

Responsibilities

• Develop and deliver machine learning solutions that enhance recommendation, matching, and personalization systems
• Own ML problems end-to-end: data exploration, feature engineering, model training, evaluation, production deployment, and iteration
• Design, train, and optimize models using PyTorch or TensorFlow for production environments
• Contribute to experimentation through A/B testing and offline evaluation, using insights to drive continuous improvement
• Build, maintain, and monitor production ML models, diagnosing issues and improving reliability at scale
• Write high-quality, maintainable code and participate in code reviews and technical discussions
• Apply responsible AI practices throughout the development lifecycle, considering fairness, transparency, privacy, and safety
• Collaborate with cross-functional partners to translate product and user problems into practical ML solutions

What We're Looking For

Required qualifications:

• Approximately 3+ years of hands-on experience building and shipping machine learning models in production environments
• Strong programming skills in Python and proficiency with PyTorch or TensorFlow
• Industry experience researching or applying ML, ideally in recommendation systems, ranking, retrieval, search, or personalization
• Solid understanding of the ML development lifecycle including data development, training, evaluation, deployment, and monitoring
• Knowledge of MLOps and infrastructure concepts such as CI/CD for ML, feature stores, model serving, observability, and versioning
• Familiarity with containerization and cloud-native environments (Docker, Kubernetes, GCP or comparable technologies)
• Experience with experimentation methodologies including A/B testing, offline evaluation, and model performance metrics
• Strong problem-solving abilities and capacity to independently navigate technically ambiguous problems
• Agile mindset with ability to adapt based on data, experimentation, and evolving priorities while maintaining focus on outcomes
• Growing AI fluency with ability to apply ML techniques and emerging technologies responsibly

Bonus qualifications:

• Practical experience building recommendation, ranking, retrieval, search, or personalization systems in production
• Experience with modern recommendation approaches such as embeddings, two-tower models, learning-to-rank, or representation learning
• Exposure to modern ML architectures including transformers, graph neural networks, contrastive learning, or multimodal embeddings
• Experience with real-time or low-latency ML inference systems at scale

How We Work With You

When you apply through Get A Job.ai, our recruiting team will review your profile and conduct an initial screening to understand your background and career goals. If there's a strong match, we'll submit your candidacy to our client for consideration and guide you through their interview process.

Please apply exclusively through Get A Job.ai. Do not attempt to contact the client directly, as we are managing this search on their behalf.

Pay

Compensation details will be discussed during the screening process and will be based on your qualifications, experience, and skill set.

Equal Opportunity

Get A Job.ai is committed to inclusive hiring practices. We welcome and encourage applications from candidates of all backgrounds, experiences, and identities.