Apply machine learning and AI techniques including GenAI and LLM-based approaches to develop model systems and solutions, collaborating across functions to scale and integrate these into large-scale cloud-based SaaS production environments for healthcare.
Work with product leaders, clinical informaticists, data scientists, UI/UX researchers, engineers, and healthcare professionals to design and deliver high value solutions.
Design, build and evaluate solutions that may involve structured or unstructured data for healthcare use cases, delivering capabilities such as predictive models, summarization, recommenders, semantic search, extraction, classification, or other AI/ML applications.
Perform research and experimentation to select appropriate approaches, algorithms, and evaluation methods, and execute the R&D needed to deliver production-ready model systems.
Perform data collection, cleaning, analysis, prompt tuning, parameter fine-tuning, model training, development, and evaluation—using existing or developing new tools or workflows as needed.
Apply and help evolve responsible AI evaluation practices, using approaches and frameworks such as LLM-as-judge, RAGAS, and LangSmith, and design and implement custom model performance and quality metrics as needed to ensure model quality, fairness, and safety at scale.
Contribute to a collaborative team culture, sharing knowledge and helping develop new capabilities across the organization.

Requirements

Master's degree or equivalent experience in Computer Science, Math, Physics, Engineering, or a related field, with strong fundamentals in applied Mathematics, statistics, or machine learning.
Proven industry experience through multiple major product releases in a commercial SaaS environment.
Hands-on experience working with healthcare data (e.g. EHR, ADT, clinical notes).
Proficiency in Python. Proficiency in Java or other languages helpful.
Proficiency with SQL and data engineering for AI/ML applications.
Experience working with large datasets using big data frameworks (e.g. Azure Data Lake, Apache Spark or Databricks)
Solid understanding of transformer models and LLM-based approaches, including hands-on experience with prompt tuning and PEFT methods (e.g. LoRA, QLoRA) using frameworks such as Hugging Face Transformers.
Experience building and evaluating models using modern ML packages such as NumPy, SciPy, Pandas, Scikit-learn, PyTorch, and LightGBM.
Experience building and deploying models using public cloud infrastructure (Azure, AWS, or Google Cloud), including familiarity with version control, CI/CD pipelines, and scaling considerations for production ML systems at SaaS scale.
Strong communication and collaboration skills; comfortable working on a distributed team.
Experience with one or more of: reinforcement learning or RLHF, NLP techniques for summarization, extraction, classification, or semantic search, retrieval-augmented generation (RAG) pipelines and/or agentic frameworks (e.g. LangChain, LlamaIndex).

Benefits

Benefits starting from Day 1!
Retirement Plan Matching
Flexible Paid Time Off
Wellness Support Programs and Resources
Parental & Caregiver Leaves
Fertility & Adoption Support
Continuous Development Support Program
Employee Assistance Program
Allyship and Inclusion Communities
Employee Recognition … and more!

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningAI techniquesGenAILLM-based approachesPythonSQLbig data frameworkstransformer modelsprompt tuningmodel evaluation

Soft Skills

communication skillscollaboration skillsteam culture

Certifications

Master's degree in Computer ScienceMaster's degree in MathMaster's degree in PhysicsMaster's degree in Engineering