Applied AI Engineer
Build and productionize AI/LLM applications end-to-end to automate insurance workflows, document classification, and patient access processes for a fast-growing healthtech startup. Requires strong ML engineering and full-stack skills.
Responsibilities
- Scope and spearhead AI augmentation and automation projects across the product surface area (classifications, data extraction/summarization, content generation, reference-based search/QA, process outcome prediction, probabilistic workflow triggering, multimodal bots)
- Drive zero-to-one product development from conceptualization through production, collaborating with go-to-market and operations teams
- Stay on top of emerging AI methods and drive decisions around models/techniques, fine-tuning, and training
- Establish research strategies including experimentation and evaluation protocols for accuracy and consistency
- Prototype and productionize AI functionality and agents, incorporating real-world feedback
- Participate in client engagements, working directly with customers
- Develop engineering process, tools, and systems to support faster AI product development (e.g., one-click eval system) and scaling (e.g., model invocation efficiency)
- Work closely with the team and CEO on business decisions balancing speed of growth and long-term profitability
Requirements
- Strong programming skills and general Computer Science knowledge
- Experience driving AI projects end-to-end — from model development and data infrastructure to production deployment and real-world iteration
- Proficiency with modern AI/ML technologies (LLM APIs, PyTorch, TensorFlow) and strong foundation in full-stack web development (Python, React, TypeScript, PostgreSQL, Kubernetes)
- Strong written and verbal communication skills
- Track record of moving quickly, finding shortcuts, and delivering on goals
- High NPS with former teammates
Key Projects
- Decipher and automate complex workflows for insurance coverage, affordability programs, and fulfillment
- Combine AI/ML approaches for high-precision document classification, unstructured data extraction, and reference-based QA
- Automate multi-step processes using RPA/scraping to navigate third-party platforms
- Build state machines handling failure modes across intertwined processes
- Make data and ML pipelines robust to variation in input data formats
- Leverage empirical data to understand opaque external systems (insurance policies)
- Create consumer-grade AI-powered experiences for patients and physicians
- Translate heterogeneous data into reliable insights for clinical indication selection, launch markets, and insurer negotiations
- Develop predictive and simulation models for clinical trial site performance, drug adoption rates, and rebate/subsidy impact
- Use real-time data to enroll criteria-matching patients and physicians in clinical studies
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