Machine Learning Scientist

We're partnering with a consumer-facing technology company operating at significant scale, supporting millions of users across multiple products. They are looking for a Machine Learning Scientist to join a high-impact People Recommendations team, working at the intersection of applied research and real-world product delivery. This role suits someone who enjoys taking ideas from research to production, owning the full model lifecycle and pushing the boundaries of modern recommendation systems in a two-sided marketplace.   As a Machine Learning Scientist, you'll design and evolve recommendation algorithms that directly shape how users connect and interact. You'll work closely with Data Scientists, Machine Learning Engineers, and Platform Engineers, acting as the bridge between research intuition and production reality. You'll have real ownership, autonomy, and influence, from early-stage experimentation through to models running in production at scale.   What You'll Be Doing Research & Prototype (0 → 1) Explore novel approaches to bilateral matching, user representation learning, and preference modelling Prototype new architectures tailored to two-sided marketplaces Stay close to state-of-the-art research and translate relevant advances from paper to prototype Build & Ship (1 → N) Evolve and improve production recommendation models, including Transformer and Graph Neural Network (GNN) based approaches Design robust evaluation frameworks that reflect real marketplace success Run large-scale experiments to test hypotheses and iterate quickly Collaborate & Influence Partner with Product to define what's technically feasible and what trade-offs matter Work closely with ML Engineers to ensure models are reliable, scalable, and production-ready Mentor others, contribute to technical direction, and influence best practice across the team   What We're Looking For Essential: MSc or PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field Strong experience designing, training, and shipping modern deep learning models Production-level Python skills and experience with frameworks such as PyTorch End-to-end ML lifecycle experience: feature engineering, training, evaluation, deployment, and monitoring Comfortable operating in ambiguous problem spaces and structuring solutions from scratch Strong communication skills and ability to work with both technical and non-technical stakeholders A thoughtful approach to AI fairness, accountability, and transparency   Nice to Have: Experience with recommendation systems, ranking, or search, especially in two-sided marketplaces Exposure to modern LLM deployment frameworks (e.g. HuggingFace TGI, vLLM, TensorRT-LLM) Production experience on GCP (Vertex AI, BigQuery, GKE) Understanding of GPU-powered workloads, container runtimes, and NVIDIA tooling