Principal Applied Scientist

** Job Description*
* We are seeking an exceptional Principal Applied Scientist with deep expertise in machine learning, large language models (LLMs), and agentic/LLM-powered application development. In this role, you will design and build ML and GenAI solutions for the healthcare domain-ranging from classical model development to LLM prompt and workflow optimization, fine-tuning, evaluation, and deployment of agentic systems. You will own the end-to-end lifecycle from prototyping through production, partnering closely with engineering to deliver scalable, reliable, and secure services.

You will collaborate with healthcare domain experts, product managers, and engineers to identify high-impact opportunities, define product requirements, and ship cutting-edge capabilities with a strong emphasis on LLMs and Generative AI. Your work will be pivotal in delivering new GenAI-powered solutions for healthcare and enterprise customers, with a focus on measurable outcomes, rigorous evaluation, and production readiness.

** Responsibilities*
* Responsibilities

Partner with Product Management to translate business requirements into technical problem statements, success metrics, and ML/LLM evaluation plans.

Drive end-to-end model development: dataset definition/labeling strategy, feature engineering, model selection, training, hyperparameter tuning, and error analysis for both classical ML and deep learning.

Develop and optimize LLM-based systems including prompt engineering, tool/function calling, RAG (retrieval, chunking, embedding selection), fine-tuning/PEFT where appropriate, and systematic prompt/model iteration using quantitative + qualitative evaluation.

Design and implement agentic workflows (planning, tool orchestration, memory, policy/guardrails, retry/fallback strategies) with strong emphasis on reliability, determinism where needed, and observability.

Own the path from research POC to production by establishing MLOps best practices: reproducible training pipelines, model/version management, CI/CD for ML, automated testing (data/model/prompt), and deployment runbooks.

Architect and review AI solution designs across data, training, serving, and evaluation-covering data quality/lineage, scalability, latency/throughput targets, cost efficiency, and secure handling of sensitive healthcare data.

Productionize models and services with engineering partners: containerized inference, batch vs online serving patterns, API design, integration testing, performance benchmarking, and capacity planning.

Establish monitoring and continuous improvement loops: drift detection, model/prompt regression testing, online quality metrics, incident response, and post-deployment iteration based on telemetry and user feedback.

Coordinate with multinational teams to deliver milestones on time, unblock dependencies, and ensure consistent engineering standards across code, documentation, and operational readiness.

Participate in planning, design reviews, and retrospectives, providing technical leadership on scope, tradeoffs, risk management, and roadmap sequencing.

Qualifications

Minimum Qualifications:

PhD Computer Science, Mathematics, Statistics, Physics, Linguistics or a related field with a dissertation, thesis or final project centered in Machine Learning Techniques OR Masters or Bachelor's in one or more of these fields. Minimum 4 years work experience in the areas of machine learning, computer vision, natural language processing or data mining with a PhD OR 5+  years experience with a Master's or Bachelor's.

Preferred Qualifications:

Scientific thinking and the ability to invent, with a track record of contributing to the advancement of the field. Demonstrated experience in successfully designing and shipping models using  machine learning, deep learning, and statistical modeling across different data domains and modalities. Experience in optimization and scaling of ML solutions for real world business use cases. Training machine learning models with large scale data using techniques such as data and model parallels.

Experience in technical leadership of data science groups/projects. Track record of innovation in creating novel…