Job Description & How to Apply Below
Location: Bengaluru
Why Zigment.ai
In today's fast-paced digital landscape, engaging and converting leads is crucial for every business. Zigment.ai provides next-generation AI conversational solutions to help businesses grow their revenue, acquire more customers, and build a stronger brand with ease.
We see a massive opportunity ahead and are committed to building a significant, lasting business. We're assembling a diverse team of curious, creative individuals who find purpose in their work and support one another. If this resonates with you, we'd love to hear from you.
Read more on https://(Use the "Apply for this Job" box below)..ai
About the Team
The AI Research team is responsible for the core intelligence behind our conversational AI platform. We research, design, and build the ML and LLM systems that power our AI agents — from language understanding and generation to reasoning, personalisation, and domain adaptation. Our work directly shapes the product and pushes what's possible in AI-driven business conversations.
About the Role
As a Research Scientist / Research Engineer , you will advance the capabilities of our AI systems through original research and hands-on model development. Your work will span the full research lifecycle — formulating hypotheses, designing experiments, training and fine-tuning models, analysing results, and translating breakthroughs into production-ready systems.
We're looking for people who have strong foundations in machine learning and NLP research, can iterate quickly on ideas, are proficient at both writing research code and engineering robust systems, and are excited about building AI that solves real business problems at scale.
This is a WFO Role in Bengaluru, KA.
You might thrive in this role if:
You love being on the cutting edge of LLM, NLP, and machine learning research.
You're a self-starter who takes ownership of research directions and drives them from idea to implementation.
You value principled approaches — simple experiments in tightly controlled settings that lead to trustworthy, reproducible conclusions.
You thrive in a fast-paced, dynamic environment where rapid iteration and creative problem-solving are key.
You're comfortable diving deep into large ML codebases to debug, improve, and extend them.
You have a deep understanding of how modern language models work and a strong intuition for what makes them better.
What You'll Need
Master's or PhD in Computer Science, Machine Learning, AI, or a closely related field (or equivalent practical experience with a strong research track record).
Deep understanding of modern LLM architectures — Transformers, attention mechanisms, decoder-only models, mixture-of-experts, etc.
Hands-on experience training, fine-tuning, or adapting large language models (techniques such as LoRA, QLoRA, RLHF, DPO, SFT, or similar).
Strong proficiency in Python and deep learning frameworks (PyTorch preferred; JAX a plus).
Experience designing and executing rigorous ML experiments with proper evaluation, ablation studies, and reproducibility.
Familiarity with the Hugging Face ecosystem (Transformers, PEFT, Datasets, TRL) or equivalent tooling.
Ability to read, critically assess, and implement ideas from recent research papers.
Strong mathematical foundations in probability, statistics, optimisation, and linear algebra.
Preferred Qualifications
Publications or significant contributions at top-tier ML/NLP venues (NeurIPS, ICML, ICLR, ACL, EMNLP, etc.).
Experience with reinforcement learning from human feedback (RLHF), reward modelling, or alignment research.
Familiarity with distributed training and scaling strategies (Deep Speed, FSDP, Megatron-LM).
Experience building or improving inference pipelines (vLLM, Tensor
RT-LLM, ONNX Runtime).
Knowledge of retrieval-augmented generation (RAG), agentic frameworks, or tool-use in LLMs.
Experience with multi-modal models (vision-language, speech-language).
Contributions to open-source ML/AI projects or publicly shared research artefacts.
Prior experience in a startup or fast-paced product environment where research directly impacts production.
What You'll Do
Research & Experiment — Formulate research questions, design experiments, and develop novel…
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