ML Researcher – Discovery

ML Researcher – Discovery & NLP Specialist

Location: Bangalore (India)

Type: Full-Time | Immediate Joining Preferred

CTC: Competitive

About YAL.ai

YAL.Ai which stands for Your Alternative Life, is a revolutionary end-to-end communication and discovery platform that redefines how people connect, interact, and collaborate. Powered by advanced AI, YAL.ai offers secure, AI-driven instant chats, group discussions, dynamic communities, and seamless VoIP calls, delivering a comprehensive and engaging communication experience.

Built on a robust Zero Trust Architecture, YAL.ai ensures maximum security by verifying every interaction and safeguarding user data at all levels. Our platform incorporates on-device AI models for enhanced privacy, zero-day fraud detection, multilingual automatic speech recognition (ASR), personalized recommendations, and comprehensive multilingual support.

YAL.Ai embodies the essence of trust and innovation with its tagline, "Where AI Meets Integrity." Users can build meaningful connections, foster vibrant communities, and explore limitless opportunities in a secure, privacy-centric digital environment. YAL.ai isn't just a platform—it's your alternative life, where communication and discovery are smarter, safer, and more impactful.

Role Overview

We are hiring a highly motivated ML Researcher – Discovery & NLP Specialist to lead and scale our AI-driven discovery systems across chats, communities, and user profiles. You will design context-aware, real-time recommendation and ranking models using state-of-the-art techniques in transformer-based NLP, graph embeddings, and personalization algorithms. Your work will directly impact how users discover people, topics, and communities in a fraud-aware, multilingual, and personalized environment.

Key Responsibilities

● Design and build intelligent discovery and recommendation systems using advanced NLP and graph-based techniques.

● Architect semantic matching systems for user–user, user–group, and content–topic discovery.

● Develop and fine-tune transformer-based models (e.g., BERT, SBERT, Twin-BERT) for profile and intent matching.

● Apply unsupervised and weakly supervised learning (e.g., UMAP + HDBSCAN) to create thematic clusters and discovery maps.

● Leverage multilingual NLP to enable cross-language discovery and code-mixed content understanding.

● Build ranking pipelines with re-ranking, confidence scoring, and source credibility modules.

● Prototype and scale recommendation systems for cold-start and high-volume discovery events.

● Collaborate with the fraud detection and ASR teams to ensure safe and meaningful suggestions in real time.

Required Technical Skills

● Strong foundation in NLP, recommendation systems, and deep learning (3+ years industry or research).

● Proficiency with Python, PyTorch or TensorFlow, HuggingFace Transformers, and vector search libraries (e.g., FAISS, Annoy).

● Experience with embedding models, BERT/SBERT variants, and sentence similarity tasks.

● Hands-on with dimensionality reduction (UMAP, PCA), clustering (KMeans, HDBSCAN), and graph-based learning (Node2Vec, DeepWalk).

● Knowledge of scalable ranking strategies using hybrid dense + sparse retrieval.

● Experience with low-latency inference, real-time API integrations, and model optimization (distillation, quantization).

● Familiarity with multilingual processing and cross-lingual embedding techniques.

Qualifications

● Master's or PhD in Computer Science, Artificial Intelligence, Computational Linguistics, or a related field.

● Strong academic or research background in NLP, discovery/recommendation systems, or transformer-based learning.

● Published papers or open-source projects in personalization, ranking, or NLP highly valued.

Experience

● 3+ years of hands-on experience in building and deploying discovery/recommendation systems.

● Proven success in NLP model design for real-time, user-facing products.

● Industry or research experience with semantic search, content-based recommendations, and hybrid retrieval systems.

● Experience working in fast-paced, production-grade environments and scaling models to millions of users.

Bonus if You Have

● Built user discovery or social graph recommendation systems.

● Familiarity with community recommendation, trust modeling, or topic-based ranking.

● Experience handling fraud-aware ranking (e.g., link manipulation, bot detection).

● Exposure to Indic languages, code-mixed NLP, and multilingual ranking pipelines.

● Contributions to Kaggle NLP challenges or relevant publications in ACL/EMNLP/RecSys.

How to Apply

Easy apply on or DM us here on , or send your CV + work samples (GitHub, papers, demos) to with subject line:

Subject: ( ML Researcher – Discovery & NLP | Your Name )