Forward Deployed ML Engineer

Responsibilities

• Design and build agentic extraction pipelines that process 500+ page patient charts (clinical notes, pathology reports, imaging reports, genomic panels) and output structured JSON per customer data dictionaries
• Own accuracy end-to-end: define evaluation datasets, run precision/recall analysis per variable, identify failure modes, and improve through agent architecture changes, prompt engineering, fine-tuning, or rule-based post-processing
• Go deep into the clinical source data - read the actual patient charts, understand how oncologists document, learn why certain data points are ambiguous and use that understanding to improve extraction
• Coordinate with customer data science and clinical teams to clarify dictionary definitions, review output quality, and close accuracy gaps
• Coordinate with internal engineering and infrastructure teams to deploy, scale, and monitor pipelines in production
• Deliver on customer timelines - this means intense sprint periods around customer deliveries followed by iteration and improvement cycles

Requirements

• 2+ years building ML/AI systems in production
• Built and deployed AI agents or multi-step LLM pipelines (not just single-call wrappers) - you should have a clear point of view on agent architectures, tool use, orchestration frameworks, and where they break down
• Strong Python - pipeline code, data processing, infrastructure glue, not just model training scripts
• PRACTICAL LLM experience: prompt engineering, fine-tuning, RAG, evaluation design
• Built evaluation frameworks for LLM based document extraction tasks (precision, recall, per-class analysis, error taxonomy)
• Willingness to become a domain expert in oncology data - this role requires going deep into clinical documentation, not just treating it as generic text
• Comfortable owning customer-facing communication alongside technical delivery - you'll talk to customer data science teams, clinical teams, and internal engineering regularly
• Can operate in high-intensity delivery sprints and manage your own time across multiple workstreams

Compensation

Compensation Range: $170K - $190K