Hippocratic AI, the generative AI company building the only model clinically safe and fast enough for patient-facing voice AI in clinical settings as benchmarked against major frontier models, launched the LLM Model Training Residency. The six-month program is designed to move experienced software engineers into frontier LLM training while on the job, through focused work on Voice AI challenges.
The program is designed to be open to more than just current Hippocratic AI engineers. Engineers joining the company become eligible for the residency after just six months. The program offers an innovative way for software engineers to develop advanced model training skills and do career defining work while on the job.
“This is a chance to do career defining work while getting real-time LLM Model Training. We believe healthcare deserves the best engineers on the planet, so that is who we hire, and who we have designed this program to attract,” said Vishal Parikh, Chief Product Officer and Co-Founder of Hippocratic AI. “If you are a top engineer who has spent years building systems that scale and ship and you want to point that talent at voice AI that actually improves patient care, there is now a path to do it here, on our infrastructure, with our model org, on the constellation of models people in this field are watching most closely.”
Program Scope
The residency is not a lecture series. Work during the residency has the potential to shape the next generation of the Polaris model, contribute directly to patient outcomes, and be featured in white papers and industry publications. Residents fine-tune models, run experiments on Hippocratic AI’s NVIDIA H200 and B300 GPU cluster, ship a real capstone, and work 1:1 with senior ML engineers from the team behind Polaris – the company’s patented constellation LLM. The residency is extra-curricular — roughly 10 hours per week alongside the resident’s primary role.
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Engineering at HAI
Hippocratic AI’s engineering team comes from top environments – Stanford, MIT, CMU, Berkeley, IIT, and the handful of programs where the bar is set globally. They cite the same reasons for choosing HAI:
- AI for good, at scale. Models that talk to real patients, expanding access to care.
- Training our own frontier models. Engineers pretrain, fine-tune, and ship every release cycle — not wrappers, not prompt layers.
- The hardest open problems in voice AI. Empathy, conversational quality, sub-second latency, and 99.9% accuracy — in the most regulated environment in tech.
- Small team, extraordinary access. Engineers work directly with co-founders and senior ML leaders.
- A platform to lead. Engineers publish white papers, speak on panels, and shape the public conversation.
- Series C momentum. One of the fastest-growing AI companies in healthcare — trusted by leading health systems, invested in by top VC firms.
- A serious partner network. Direct collaboration with NVIDIA, Google, DigitalOcean, and Modular.
The engineering and model training work at Hippocratic AI is specific and challenging. A frontier base model is not enough for clinical voice AI. Base-model accuracy in healthcare tops out well below the 99.9% bar that medical, patient-facing AI demands. Polaris is constellation architecture pairing a primary conversational model with specialist support models for medication, labs, dosage, social determinants, privacy, compliance, and safety. The specialists check the primary in real time, deterministically, on every clinical claim. Polaris also drives the speech stack: low-latency, empathetic, interruption-aware voice at clinical speed.
“This is one of the few places in the industry where an engineer can build voice AI from the model layer up, on production hardware, in a domain that demands real safety guarantees,” said Subhabrata Mukherjee, Co-founder and Chief Scientific Officer. “Empathy, conversational quality, sub-second latency, and 99.9% accuracy are not solved problems. We are solving them through Polaris’s constellation architecture, through speech models tuned for clinical conversation, and through evaluation infrastructure designed for the regulated environment we operate in. Residents step directly into that work, alongside the team that built it.”
Source: PRNewswire