Job Title

Machine Learning Engineer – Recommendation Systems

Location

Bangalore

Experience

3–8 years (flexible based on depth in ML systems)

Job Description

We are looking for a Machine Learning Engineer (Recommendations) to design, build, and scale personalized recommendation systems that power discovery, ranking, and user engagement across our products. You will work at the intersection of machine learning, data engineering, and backend systems, taking models from research to production.

Key Responsibilities

Recommendation & ML

• Design and develop recommendation systems including:
• Collaborative Filtering (user-item, item-item)
• Content-based and hybrid recommenders
• Ranking and re-ranking models
• Embedding-based retrieval (ANN, vector search)
• Train, evaluate, and iterate on models using offline metrics (NDCG, MAP, Recall@K) and online A/B experiments
• Build pipelines for feature engineering, model training, inference, and retraining
  

Production ML & Systems

• Deploy ML models in production environments with low-latency constraints
• Optimize inference for scale (caching, batching, approximate nearest neighbors)
• Build real-time and batch recommendation pipelines
• Monitor model performance, data drift, and system health
  

Data & Experimentation

• Work with large-scale datasets (clicks, impressions, transactions)
• Define success metrics for recommendations (CTR, CVR, retention)
• Run and analyze A/B tests and iterate based on results.

Collaboration

• Work closely with product, data, and backend teams to translate business problems into ML solutions
• Contribute to ML best practices, documentation, and system design
  

Required Skills

Core ML

• Strong understanding of:
• Recommendation algorithms
• Ranking and learning-to-rank
• Embeddings and similarity search
• Experience with Python and ML libraries (PyTorch / TensorFlow / Scikit-learn)
  

Data & Systems

• Strong SQL skills; experience with large datasets
• Experience with feature stores, data pipelines, and batch/stream processing
• Familiarity with vector databases / ANN libraries (FAISS, ScaNN, Elasticsearch/OpenSearch KNN, Milvus)
  

Production & Infra

• Experience deploying models using REST/gRPC services
• Familiarity with Docker, Kubernetes, or cloud platforms (AWS / GCP / Azure)
• Understanding of latency, throughput, and scalability trade-offs
  

Good to Have

• Experience with:
• Search or feed ranking systems
• Hybrid retrieval (BM25 + embeddings)
• Real-time recommendations
• Knowledge of:
• Kafka / streaming systems
• MLOps tools (MLflow, Airflow)
• Experience in e-commerce, ads, content platforms or marketplaces
  

What You’ll Work On

• Personalized home feeds and search ranking
• “People also viewed” recommendations
• Cold-start and long-tail problems
• Large-scale experimentation and model optimization
  

Nice Behavioral Traits

• Strong problem-solving and system-thinking mindset
• Ability to balance model quality vs production constraints
• Comfortable owning models end-to-end