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Forward Deployed ML Engineer - HealthTech / Series A at division50
New York, New York, United States -
Full Time

Start Date

Immediate

Expiry Date

11 Aug, 26

Salary

0.0

Posted On

13 May, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Python, Prompt Engineering, Fine-tuning, RAG, LLM Evaluation, AI Agents, ML Pipelines, Clinical NLP, Biomedical NLP, Customer Facing Communication

Industry

Business Consulting and Services

Description
This role is for our partner. [the company] is building the agentic AI layer for oncology EHRs. Cancer hospitals spend billions on highly trained staff manually reading unstructured patient records - pathology reports, clinical notes, genomic panels - to power workflows like trial matching, registry curation, visit prep, and quality reporting. We replace that manual work with task-driven AI agents that sit inside the EMR and process records at scale, in real time. Our platform is trusted by the 4 of the top 10 Best Hospitals for Cancer by U.S.News and several of the largest community practices. We have grown 10x in the last year and process millions of oncology medical documents monthly. Build and deploy AI agent pipelines that extract structured oncology variables from unstructured patient documents. You own the full cycle: understanding the customer's data dictionary, studying the source clinical documents, building extraction agents, evaluating accuracy, deploying to production, and iterating until it works. - 2+ years building ML/AI in production - Built AI agents or multi-step LLM pipelines - Strong Python - Prompt engineering, fine-tuning, RAG, eval design - Evaluation frameworks for LLM document extraction - Willingness to become oncology-domain expert - Customer-facing comfort - High-intensity delivery sprints Nice to have: Kept up with agentic ML landscape; clinical or biomedical NLP.
Responsibilities
Build and deploy AI agent pipelines to extract structured oncology variables from unstructured patient documents. Manage the full lifecycle from understanding customer data dictionaries to production deployment and iteration.