Research Engineer
Research Engineer building production LLM and ML systems for healthcare workflows. Requires strong ML/NLP research background with publications, production deployment experience, and proficiency in PyTorch/TensorFlow/JAX.
About the job
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
Skills
PyTorch, TensorFlow, JAX, Transformer Architectures, Reinforcement Learning, Multimodal Models, NLP, LLMs, Few-Shot Learning, Agentic Reasoning
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