Applied ML Engineer

About the role

We are looking for an Applied ML Engineer with strong experience in recommender systems to build the brain of PlaySuper's in-game commerce store — a recommendation engine that decides which products, coupons, and rewards to surface to which player, at which moment. This role is ideal for someone who has worked on:

What you'll do

• Own the collaborative filtering model (starting with Gorse, potentially moving to a custom stack)
• Build product embeddings (product2vec + Faiss / ANN) for the PlaySuper catalogue
• Evolve cohort assignment from rules-based to ML-driven
• Build the offline evaluation framework — precision@k, NDCG, conversion-rate, diversity, coverage
• Bridge offline models to online serving (model serving infrastructure, weekly refresh pipeline)

What you need

• 3–5 years in data science & ai
• Machine learning
• Python
• Scikit-learn
• PyTorch
• NumPy
• Redis
• Any graduate