Research Engineer

Building Healthcare That Remembers

Every time you call a doctor's office, the system forgets you the moment you hang up. You wait on hold, explain everything from scratch, get bounced between lines, and hope someone follows up. Everyone has felt this. No one should have to.

At Assort Health, we're building one continuous conversation for every patient. An AI agent that knows who you are, remembers how you like to be reached, and carries that context from your first symptom to your full recovery. What began as the first voice AI agent to schedule a specialty appointment has become the largest proprietary specialty dataset in healthcare: 285 million patient interactions, 62,000 care protocols, 1.6 million decision pathways.

We went from 15 people in 2025 and will cross 250 by the end of 2026. Revenue is up 20x in 15 months and we've raised $222 million, including a recent $120 million Series C at a $1.2 billion valuation. We've also been named to the Forbes Cloud 100 and the 2026 Enterprise Tech 30 as the only healthcare AI company on the list!

Healthcare spends $1.1 trillion a year on administration instead of care.

As a Research Engineer, you’ll be responsible for building industry-leading healthcare AI models and model pipelines that power Assort’s voice agents and overall patient platform, taking them all the way from idea to production. You’ll own large initiatives that push the boundary of what models and agents can accomplish in the healthcare space while maintaining high levels of reliability, efficiency, and precision of accomplishing tasks.

We’re looking for strong research engineers who want to build the core models and algorithms behind our core platform and agents. People here own their work end-to-end, ship real improvements, and are trusted to make high-impact technical decisions.

• Lead research and engineering efforts to improve core conversational capabilities in production, including instruction following, tool calling, retrieval, and memory
• Investigate research questions in speech and audio, and design experiments to test hypotheses about model behavior and capabilities
• Build and iterate on end-to-end models and pipelines that optimize for quality, efficiency, and user experience
• Identify opportunities for model-task optimization to ensure the right model architectures are used for different tasks based on performance, latency, and cost
• Partner with platform and product engineers to integrate new models into production systems
• Break down ambiguous research ideas into clear, iterative milestones and roadmaps
• Train, fine-tune, validate, and further develop models built in-house at Hume and models developed by our customers
• Design evaluation protocols and benchmarks that measure model quality, generalization, robustness, and progress against research objectives
• Analyze datasets, model outputs, and failure cases; compare training approaches and use the evidence to guide the next round of model development

• 5+ years of experience with a minimum of 3+ in AI/ML engineering or research
• Prior experience post-training and deploying LLMs in production environments
• Fluency in Python and modern ML tooling (training, evaluation, inference, data pipelines)
• Track record of taking research ideas from prototype → reliable, measurable production impact
• Strong experimental judgment: formulating hypotheses, establishing useful baselines, designing evaluations, and interpreting results carefully
• (Bonus) Prior experience utilizing and improving speech-to-text (STT), text-to-speech (TTS) models, and/or duplex models

A PhD is not required. We value demonstrated research ability and the quality of the models, experiments, and systems you have helped build.