Design build and fine-tune AI systems that power real healthcare impact. Success looks like shipping reliable models to production improving accuracy/latency/cost and running reproducible experiments. This role sits within the core AI team and partners with product data and engineering to turn research into scalable products.
Responsibilities
Design train fine-tune and evaluate models (LLMs embeddings) for production use cases.
Build reproducible data/ETL pipelines and experiment tracking.
Implement MLOps best practices (versioning CI/CD for models monitoring rollback).
Run benchmarking A/B tests error analysis; document findings and decisions.
Create internal tools/SDKs for inference evaluation and prompt engineering.
Deploy via REST APIs and optimize for performance and cost.
Communicate results to technical and non-technical stakeholders.
Nice to have: C/Java; publications or patents; Unreal Engine exposure.
Environment: Solid Linux background for development and deployment.
Education/Experience: Relevant degree or equivalent practical experience.
Role Summary Design build and fine-tune AI systems that power real healthcare impact. Success looks like shipping reliable models to production improving accuracy/latency/cost and running reproducible experiments. This role sits within the core AI team and partners with product data and engineering ...
Role Summary
Design build and fine-tune AI systems that power real healthcare impact. Success looks like shipping reliable models to production improving accuracy/latency/cost and running reproducible experiments. This role sits within the core AI team and partners with product data and engineering to turn research into scalable products.
Responsibilities
Design train fine-tune and evaluate models (LLMs embeddings) for production use cases.
Build reproducible data/ETL pipelines and experiment tracking.
Implement MLOps best practices (versioning CI/CD for models monitoring rollback).
Run benchmarking A/B tests error analysis; document findings and decisions.
Create internal tools/SDKs for inference evaluation and prompt engineering.
Deploy via REST APIs and optimize for performance and cost.
Communicate results to technical and non-technical stakeholders.