AI/ML Scientist / Engineer — LLM Post-Training

AI/ML Scientist / Engineer — LLM Post-Training

Sahai | eGovernments Foundation

About Sahai

Sahai is an initiative backed by the eGovernments Foundation, building an AI triage system and clinician co-pilot to expand primary healthcare access across rural India.

Through voice-based conversations, Sahai gathers patient histories and prepares differential diagnoses and draft care recommendations for qualified doctors to review. We are developing open-weight models suited to Indian primary care, supported by clinical data, safety controls, and rigorous evaluation.

The AI recommends. The doctor decides.

The Role

We are looking for a hands-on AI/ML Scientist / Engineer to develop and validate a proof-of-concept model for clinical interviewing and diagnostic decision support.

The challenge goes beyond teaching models medical facts. It involves improving their ability to conduct useful multi-turn patient interviews, work with clinical tools, and reason with incomplete information.

You will collaborate with clinicians, data specialists, and engineers to design experiments, post-train candidate models, and establish what works. This role focuses on model research and development, not production deployment or operational infrastructure.

What You Will Do

• 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.

What You Bring

• 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.We value demonstrated technical depth and experimental rigor more than familiarity with a long list of tools. Prior production-deployment experience is not required.

Especially Valuable

• 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.A medical background is not required. Respect for clinical expertise and responsible handling of sensitive data are essential. Speech-model training is not the primary focus of this role.

What Success Looks Like

An evaluated proof of concept that demonstrates measurable improvements over a baseline, supported by reproducible experiments and an honest account of its limitations. The work should help Sahai decide which approaches merit further development; production readiness is not the expected outcome.

Why Sahai

Apply advanced AI research to a concrete public-health challenge. Help establish the model capabilities that could bring qualified medical judgment within reach of underserved communities.