Experience Not stated in the posting - worth applying if the stack matches
About the role
structured by ORIML Research Engineer; Post-training (LLMs)Bengaluru · Full-time · Experience: 4–8 yrsIndia's healthcare runs in twenty-two languages, on handwritten prescriptions and ten-minute consults, and the models that should serve it are trained on the English internet. We're fixing that, in the open.About EkaCareEkaCare is…
What you will do
- Design SFT/IFT data mixes and chat templates for clinical tasks, and reformat the world's medical data to match.
- Run preference optimisation (DPO/ORPO/GRPO-class) and reward-model training; own the ablation grid.
- Build RL loops with verifiable medical rewards, with practising physicians in the loop. Real doctor-in-the-loop, not proxy labels.
- Create agentic and tool-use training data for healthcare workflows.
- Live in the eval → error-analysis → iterate loop; red-team your own model before the world does.
What they are looking for
- 2–4 years in ML with hands-on post-training of ≥7B open-weights models; you've shipped SFT plus at least one preference-optimisation method end to end, not a notebook demo.
- Fluency with the open post-training stack (TRL / NeMo RL / OpenRLHF; vLLM for rollouts).
- Strong empirical taste: ablation discipline, LLM-judge literacy, contamination paranoia.
- Solid engineering, Python, distributed-training basics, comfort in a fast codebase.
Nice to have
- PPO/GRPO at scale; reward-hacking war stories.
- Multilingual or medical/clinical alignment work.
- Open-source contributions people actually use.
MLSFTDPOORPOGRPORLPythondistributed trainingTRLNeMo RLOpenRLHFvLLM
Full posting text
Experience: 2 - 4