Applied ML Scientist

ABOUT SAIVA AI

SAIVA AI applies machine learning to make optimal use of electronic health data for the most vulnerable healthcare population. Our mission is to improve patient outcomes by augmenting clinical decision-making with the power of AI.

Based in Silicon Valley, our team is a group of passionate healthcare technology veterans, engineers, and data scientists, including from Stanford University, leveraging cutting edge technology to predict patient risk and provide tools that drive timely intervention.

POSITION DESCRIPTION

As a Senior Applied ML Scientist, you will build and improve models across structured and unstructured clinical data, pairing boosting models with language models to lift real-world impact. You’ll prototype fast, run rigorous evaluations, and deliver sound research code that our MLEs will harden and optimize and our MLOps will ship and monitor.

RESPONSIBILITIES

In this position you will :

Develop or enhance high-dimensional boosting models; feature engineer, calibrate, and improve recall or precision on imbalanced data.

Prototype with LLMs; distill / quantize them to meet latency + cost SLOs

Own evaluation : offline backtests, cohort / threshold analysis, calibration, decision curves; document and communicate trade-offs clearly.

Collaborate with MLEs for serving; provide clean correct artifacts (checkpoints, configs, eval harnesses).

Work hands-on in Python, Jupyter, SQL, Git, MLflow on AWS (S3 / EC2 / ECS / CPUs / GPUs)

REQUIREMENTS

4+ years post-undergrad industry experience as a hands-on IC focusing on MLR.

Track record with boosting + Transformers / LLMs (API prototyping and smaller-model fine-tuning).

Evidence you’ve hit accuracy + cost / latency targets at scale.

Comfortable proposing new modeling approaches and new prediction targets.

Undergrad degree required in Computer Science, Data Science, Statistics, Econometrics, Physics, Applied Math, or Electrical Engineering.

Nice to have : prior healthcare or outcomes work.