Research Engineer

As a Research Engineer on our team, you will work on real production use cases of LLMs and other ML techniques to solve business problems and create groundbreaking AI applications. The role requires that you develop a deep understanding of our product surface area and what drives our business, such that you can operate and drive impact both cross-functionally and independently.

You will have end-to-end responsibility for projects, including definition, design, development, launch, and success––this includes ensuring your output has the expected impact on user growth, operational efficiency, or revenue generation.

- Scope and spearhead AI augmentation and automation projects across our product surface area, including: Unintuitive classifications, Data extraction and summarization, Precise content generation, Reference-based search and question answering, Process outcome prediction, Probabilistic triggering of workflows, and Multimodal LLM-powered bots

- Stay on top of emerging AI methods and guide decisions around which models and techniques to adopt––including evaluating when to use open-source models, proprietary models, and custom fine-tuning approaches

- Establish research strategies for various AI methods, including rigorous experimentation and evaluation protocols that account for accuracy, consistency, interpretability, and real-world impact

- Develop novel algorithms and techniques to address core research problems in natural language processing, data extraction, and autonomous reasoning (e.g., few-shot learning, agentic reasoning, and multi-modal interaction)

- Decipher and automate complex, branching workflows for insurance coverage, affordability programs, and fulfillment

- Combining AI/ML approaches to achieve high precision document classification, unstructured data extraction, and reference-based question answering

- Automating multi-step, path-dependent processes, using a combination of RPA/scraping approaches to navigate and operate third-party platforms

- Building a state machine that drives system decisions and handles failure modes across a set of processes that are technically independent but practically intertwined

- Scale across a growing range of drug classes, patient populations, and provider markets

- Making our data and ML pipelines robust to variation and inconsistency in input data formats (e.g., clinical documentation structure and style)

- Leveraging empirical data to build and continuously update our understanding of opaque external systems (e.g., insurance company policies)

- Creating consumer-grade experiences for patients, physicians, and other users that incorporate intuitive AI-powered workflows

- Use our network to help biopharma partners accelerate drug development, launch, and access

- Translating large volumes of heterogeneous data into reliable insights, informing decisions like clinical indication selection, launch markets, and insurer negotiations

- Developing predictive and simulation models to forecast outcomes such as clinical trial site performance, drug adoption rates, and the impact of rebates/subsidies

- Using real-time data and direct engagement channels to enroll criteria-matching patients and physicians in clinical studies and access programs

- Strong programming skills and general Computer Science knowledge

- Research background in ML/NLP, demonstrated through publications in top-tier conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP) or significant open-source contributions

- Experience working on complex ML problems (e.g., data-efficient learning, reasoning agents, or multi-step workflows) and deploying those solutions in production environments

- Deep understanding of modern ML methods, including transformer architectures, attention mechanisms, reinforcement learning, and multimodal models — with proficiency in deep learning frameworks such as PyTorch, TensorFlow, or JAX