Machine Learning Engineer

RemoteStar is Hiring: Applied ML Engineer Recommender Systems

Location: Gurgaon | Full-time | In-office

For one of our fast-growing gaming commerce clients

RemoteStar is hiring an Applied ML Engineer for one of our clients Indias first Gaming Commerce company, building a new way for 500M+ gamers to shop inside games.

Our client works with game studios and brands to turn in-game engagement into real-world rewards, helping studios improve monetization, players discover better rewards, and brands drive measurable performance.

Were looking for someone to build the recommendation engine behind an in-game commerce store deciding which products, coupons, and rewards to show to which player, at the right moment.

What youll work on:

• Own collaborative filtering models, starting with Gorse and potentially moving to a custom stack
• Build product embeddings using product2vec, Faiss / ANN, and related retrieval systems
• Develop ML-driven cohort assignment and ranking systems
• Build offline evaluation frameworks using precision@k, NDCG, conversion rate, diversity, and coverage
• Bridge offline models to online serving through model-serving infrastructure and refresh pipelines
• Calibrate recommendations against business outcomes such as CTR, GMV, margin, and repeat redemption
• Work closely with Data Engineering and Backend teams on event pipelines, feature stores, ranking APIs, and Redis-based serving layers
• Translate sparse and noisy in-game event data into reliable recommendation signals

What were looking for:

• 35 years of ML engineering experience, specifically in recommender systems
• Strong Python skills: PyTorch / JAX, scikit-learn, NumPy
• Hands-on experience with collaborative filtering, sparse matrices, cold start, and production evaluation
• Experience with embedding-based retrieval using Faiss, ScaNN, or similar tools
• Strong understanding of recommendation evaluation beyond accuracy diversity, coverage, and business metrics
• Experience moving models from offline notebooks to production serving
• Clear communication and structured problem-solving

Nice to have:

• Experience with voucher, coupon, deal, marketplace, or content-feed recommendation systems
• Gaming, mobile, or consumer-engagement product experience
• Familiarity with Gorse or LightFM
• Experience with contextual bandits, online learning, or feature store patterns
• Startup experience and strong ownership mindset