Overview

AI/ML Scientist / Engineer — LLM Post-Training

Responsibilities

• Post-training and alignment: Design and run experiments using supervised fine-tuning, reinforcement learning, preference optimization, and reward design.
• Agentic model development: Improve language models for sequential patient interactions and tool-augmented clinical workflows.
• Knowledge distillation: Explore transferring capabilities from larger teacher models into smaller, computationally efficient models.
• Training data and simulation: Partner with clinical and data teams to develop curated datasets, synthetic conversations, and patient-simulation environments.
• Rigorous evaluation: Compare models and approaches through reproducible experiments, automated assessments, and clinical review.
• India-specific adaptation: Investigate adaptation to the communication patterns, languages, and resource constraints of Indian primary care.
• Research handover: Deliver model checkpoints, experimental code, evaluation results, and clear documentation of findings, limitations, and recommended next steps.

Qualifications

• A strong research or industry background in machine learning, with hands-on ownership of substantial model-development work.
• Demonstrated experience post-training open-weight LLMs, beyond prompting or integrating model APIs.
• Practical experience with reinforcement learning or preference optimization for LLMs, including reward design, alongside supervised fine-tuning.
• Strong Python skills and proficiency in PyTorch or JAX/Flax.
• Experience running and debugging GPU-based training experiments, with an understanding of compute and memory constraints.
• Sound judgment in data curation, experimental design, and model evaluation.
• The ability to read research critically, implement promising methods, and test whether they work in practice.
• Experience with knowledge distillation, smaller language models, or model compression.
• Work on multi-turn agents, simulated environments, or multi-agent evaluation.
• Experience with distributed training.
• Work in healthcare AI or another safety-critical domain.
• Experience with multilingual or Indic-language models.
• Relevant publications, open-source contributions, or substantial independent projects.