Research Engineer

Responsibilities

• Scope and spearhead AI augmentation and automation projects across product surface area: unintuitive classifications, data extraction and summarization, precise content generation, reference-based search and question answering, process outcome prediction, probabilistic triggering of workflows, multimodal LLM-powered bots
• Stay on top of emerging AI methods and guide decisions on model adoption, including evaluating open-source vs proprietary models and custom fine-tuning approaches
• Establish research strategies for AI methods with rigorous experimentation and evaluation protocols accounting 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 (few-shot learning, agentic reasoning, multi-modal interaction)
• Participate actively in client engagements, working directly with customers to understand requirements and deliver innovative solutions
• Work closely with team and CEO to make business decisions balancing speed of growth and long-term profitability

Key Projects

• Decipher and automate complex, branching workflows for insurance coverage, affordability programs, and fulfillment
• Combine AI/ML approaches to achieve high precision document classification, unstructured data extraction, and reference-based question answering
• Automate multi-step, path-dependent processes using RPA/scraping approaches to navigate third-party platforms
• Build state machines that drive system decisions and handle failure modes across technically independent but practically intertwined processes
• Scale across growing range of drug classes, patient populations, and provider markets
• Make data and ML pipelines robust to variation and inconsistency in input data formats (clinical documentation structure and style)
• Leverage empirical data to build understanding of opaque external systems (insurance company policies)
• Create consumer-grade experiences incorporating intuitive AI-powered workflows
• Use network to help biopharma partners accelerate drug development, launch, and access
• Translate large volumes of heterogeneous data into reliable insights informing clinical indication selection, launch markets, and insurer negotiations
• Develop predictive and simulation models to forecast outcomes such as clinical trial site performance, drug adoption rates, and impact of rebates/subsidies
• Use real-time data and direct engagement channels to enroll criteria-matching patients and physicians in clinical studies and access programs

Requirements

• Strong programming skills and general Computer Science knowledge
• Strong research background in ML/NLP, demonstrated through publications in top-tier conferences (NeurIPS, ICML, ICLR, ACL, EMNLP) or significant open-source contributions
• Experience working on complex ML problems (data-efficient learning, reasoning agents, or multi-step workflows) and deploying solutions in production environments
• Deep understanding of modern ML methods including transformer architectures, attention mechanisms, reinforcement learning, and multimodal models
• Proficiency in deep learning frameworks such as PyTorch, TensorFlow, or JAX
• Strong written and verbal communication skills for internal debates and external relationships
• Track record of moving quickly, finding shortcuts, and going to unreasonable lengths to deliver on goals