Machine Learning Engineer - Recommendation Systems

Apna • Bengaluru, Karnataka, India • 1d ago

Why This Job is Featured on The SaaS Jobs

Recommendation systems sit at the core of many SaaS products because they directly shape discovery, ranking, and engagement across recurring user journeys. This Machine Learning Engineer role stands out for its clear focus on production-grade personalization, spanning retrieval, ranking, and online experimentation rather than isolated model development. The emphasis on low-latency inference and scalable pipelines signals a product environment where ML outcomes are measured in user behavior and system performance.

For a SaaS career, the durable value here is end-to-end ownership across the ML lifecycle: feature engineering, training, deployment, monitoring, and iteration through A/B tests. Experience with metrics like CTR, CVR, and retention builds fluency in how SaaS teams connect model changes to product outcomes. Working with large-scale behavioral data and real-time or batch pipelines also translates well across subscription products that rely on continuous optimization.

This role is best suited to an engineer who enjoys operating at the boundary of modeling and backend systems, and who prefers measurable impact over purely research-oriented work. It fits someone comfortable navigating trade-offs between quality, latency, and scale, and collaborating closely with product and data partners to turn ambiguous goals into testable improvements.

The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.

Job Description

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)