AI/ML Engineer

United States·New Yorkmid

Data ScienceOtherMachine Learning EngineerData

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Quick Summary

Overview

About Us: Savant is transforming how healthcare and life sciences organizations unlock the value trapped in unstructured medical data. Our platform combines cutting-edge large language models (LLMs) with domain-specific quality controls to convert free-text clinical records into structured,…

Requirements Summary

3+ years of hands-on experience building and deploying ML/AI systems Strong Python development skills, including experience with production-quality ML codebases Familiarity with modern ML/NLP frameworks (e.g., PyTorch, Hugging Face, spaCy)…

Technical Tools

huggingfacepythonpytorchmachine-learning

Design and build robust ML pipelines to apply LLMs and other models to unstructured clinical text
Develop and optimize systems for information extraction, entity resolution, data normalization, and QA
Build and scale infrastructure for running model inference across large datasets
Prototype, evaluate, and refine prompt strategies, model configurations, and workflows
Implement internal tools for monitoring, observability, and iterative model improvement
Translate research prototypes into reliable, testable, and maintainable production code
Collaborate cross-functionally with clinical experts and product leads to embed domain knowledge into model workflows

3+ years of hands-on experience building and deploying ML/AI systems

Strong Python development skills, including experience with production-quality ML codebases
Familiarity with modern ML/NLP frameworks (e.g., PyTorch, Hugging Face, spaCy)
Experience working with large-scale unstructured or semi-structured data
Practical knowledge of LLMs: prompt engineering, evaluation, fine-tuning, observability
Comfort working in fast-paced, ambiguous environments where iteration is key
Clear communicator with a collaborative mindset

Nice to Have

~1 min read

Experience working with clinical or healthcare data (EMRs, registries, imaging, labs, etc.)
Familiarity with model evaluation tooling, quality benchmarking, and drift detection
Exposure to data privacy or compliance frameworks (HIPAA, de-ID workflows, etc.)
Experience in an early-stage startup or small, fast-moving technical team