Research Engineer

About the Role

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.

What You’ll Do

• 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

What We’re Looking For

• 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.