Senior/ AI Scientist - Hiring across various levels

Our client is a product-focused technology company building large-scale AI systems that power personalized discovery and search for its global user base. The team is now scaling its core AI capabilities to redefine how users find, discover, and engage with content.

You will work on core AI that powers what millions of users see, search, and engage with daily. This is not a service role - this is a core product AI role with direct impact on user metrics, retention, and revenue.

What You Will Do:

1. AI Model Development & Research:

• Design, develop, and deploy state-of-the-art ML/DL models for Recommendations, Ranking, Search Relevance, Query Understanding, and User Personalization.
• Work across the full ML lifecycle: problem formulation, data exploration, feature engineering, model training, offline evaluation, online A/B testing, and production deployment.
• Build models for Learning-to-Rank (LTR), Two-tower retrieval, Collaborative Filtering, Sequential Recommendations (SASRec, BERT4Rec), Click-Through-Rate (CTR) & Conversion Rate (CVR) prediction.

2. Search & Recommendations Systems:

• Improve search relevance, auto-complete, query rewriting, spell correction, and semantic search using LLMs and vector embeddings.
• Develop personalized recommendation systems for feed, content discovery, and user-to-user / user-to-item matching.
• Optimize for multiple objectives: relevance, diversity, freshness, fairness, and long-term user engagement.

3. GenAI & LLM Integration:

• Apply LLMs, RAG, and embedding models to enhance search understanding and recommendation explainability.
• Fine-tune foundation models for domain-specific tasks like intent classification, content understanding, and conversational search.
• Evaluate LLMs for latency, cost, and quality trade-offs for large-scale production.

4. Data & Engineering Excellence:

• Work with massive scale datasets (user behavior, content, graph data) and collaborate with Data & ML Platform teams.
• Define and track key metrics: NDCG, MAP, Precision@K, Recall, AUC, engagement, retention.
• Write production-grade code in Python and work with ML frameworks - PyTorch, TensorFlow, Hugging Face, Spark, Ray.

What We Are Looking For (Must-Have):

• Education: MS / PhD in Computer Science, AI/ML, Statistics, Mathematics or related field. Exceptional Bachelors with strong product experience will also be considered.
• Experience: 2-12 years in Applied ML / AI Scientist roles in a product-based tech company.
• Core ML Skills: Strong fundamentals in Machine Learning, Deep Learning, NLP, and Recommender Systems.
• Tech Stack: Expert in Python, SQL, PyTorch/TensorFlow, Scikit-learn. Experience with vector databases (Pinecone, Milvus, FAISS) and big data tools (Spark, Hive).
• Proven Impact: Hands-on experience building and shipping models for at least one of: Search Ranking/Relevance, Recommendations/Personalization, User Search/Intent, Ads Ranking.
• Evaluation: Strong understanding of offline and online experimentation, A/B testing, and causal inference.

Highly Preferred / Differentiators:

• Publications at top conferences - NeurIPS, ICML, KDD, SIGIR, RecSys, WWW, ACL
• Experience with multi-stage ranking architecture (Retrieval -> Pre-rank -> Rank -> Re-rank)
• Experience with LLMs for search (query embeddings, dense retrieval, E5, Contriever)
• Experience optimizing for low-latency inference at scale (model quantization, distillation, ONNX/TensorRT)
• Contributions to open-source ML projects or Kaggle Grandmaster/Master level