Machine Learning Engineer, Matching & Recommendations

Location: London, UK

Company

hirify.global develops dating, friendship, and relationship products through hirify.global Date, BFF, and Badoo.

What you will do

• Develop and deliver machine learning solutions for matching, recommendations, ranking, retrieval, search, and personalisation.
• Own ML problems end-to-end, including data exploration, feature engineering, model training, evaluation, deployment, and iteration.
• Design, train, optimise, maintain, and monitor production models using PyTorch or TensorFlow.
• Run A/B tests and offline evaluations to improve model and product performance.
• Diagnose reliability and performance issues in production ML systems, including real-time and low-latency inference.
• Collaborate with Machine Learning, Engineering, Product, and Data partners while contributing to code reviews and engineering practices.

Requirements

• 3+ years of hands-on experience building and shipping machine learning models in production, or equivalent demonstrated skills and experience.
• Strong Python programming skills and proficiency with PyTorch or TensorFlow.
• Experience with production recommendation systems, ranking, retrieval, search, or personalisation.
• Understanding of the ML lifecycle, including data and feature development, training, evaluation, deployment, monitoring, and iteration.
• Knowledge of MLOps concepts such as CI/CD for ML, feature stores, model serving, observability, and versioning.
• Familiarity with Docker, Kubernetes, GCP, experimentation methods, and responsible AI practices.

Nice to have

• Experience with embeddings, two-tower models, learning-to-rank, or representation learning.
• Exposure to transformers, graph neural networks, contrastive learning, multimodal embeddings, or LLMs.
• Experience building real-time or low-latency ML inference systems at scale.

Culture & Benefits

• Work on products designed to support healthy and equitable relationships and friendships.
• Apply responsible AI principles, including fairness, transparency, privacy, and member safety.
• Use data, experimentation, and evolving priorities to guide an agile, outcome-focused approach.
• AI-supported recruitment tools may assist with transcription, summarisation, and job alignment; hiring decisions are made by people.
• Reasonable adjustments are available throughout the hiring process.