Machine Learning Engineer

SHIFT:

Day (United States of America)

Seeking Breakthrough Makers

Children’s Hospital of Philadelphia (CHOP) offers countless ways to change lives. Our diverse community of more than 20,000 Breakthrough Makers will inspire you to pursue passions, develop expertise, and drive innovation.

At CHOP, your experience is valued; your voice is heard; and your contributions make a difference for patients and families. Join us as we build on our promise to advance pediatric care—and your career.

CHOP’s Commitment to Diversity, Equity, and Inclusion

CHOP is committed to building an inclusive culture where employees feel a sense of belonging, connection, and community within their workplace. We are a team dedicated to fostering an environment that allows for all to be their authentic selves. We are focused on attracting, cultivating, and retaining diverse talent who can help us deliver on our mission to be a world leader in the advancement of healthcare for children.

We strongly encourage all candidates of diverse backgrounds and lived experiences to apply.


A Brief Overview

The Campbell Laboratory at the Children’s Hospital of Philadelphia is seeking a Machine Learning Engineer to help advance our mission to diagnose rare genetic diseases more quickly and accurately. We develop and train large language models (LLMs) to better understand clinical data from the electronic health record (EHR) and to identify ways to facilitate accurate, equitable diagnoses for every child—especially those from historically marginalized backgrounds.

As a Machine Learning Engineer, you will work closely with data scientists, clinicians, and other researchers to design, implement, and scale state-of-the-art machine learning workflows. You will utilize our on-premises GPU/SLURM cluster and cloud-based TPU instances (Google Cloud) to train and deploy LLMs using Hugging Face Transformers, PyTorch, and JAX. This role combines robust software engineering practices with advanced machine learning and natural language processing (NLP) techniques, with a focus on reproducibility and high-quality code.

Our innovative and interdisciplinary environment values diversity, fosters professional growth, and drives impactful research that benefits children worldwide. If you are passionate about building robust machine learning systems, enjoy working on high-impact problems, and thrive in a collaborative research environment, we encourage you to apply.

What you will do

• · Configure and utilize on-premises SLURM cluster with GPU resources to ensure efficient and reliable job scheduling for large-scale model training.

• · Manage and optimize cloud-based infrastructures (e.g., TPU Pods on Google Cloud) for distributed model training and evaluation.

  · Collaborate with data scientists to implement and fine-tune LLMs (e.g., Transformer architectures in PyTorch, TensorFlow, or JAX) for clinical and biomedical NLP tasks.

  · Develop efficient training pipelines, including data loading, preprocessing, feature extraction, and model deployment.

  · Evaluate model performance and optimize hyperparameters, GPU/TPU utilization, and distributed training strategies.

  · Collaborate cross-functionally with clinicians, data scientists, analysts, and IT teams to support and enhance machine learning operations (MLOps).

  · Work with relational databases (e.g., Snowflake, BigQuery, Oracle SQL, MySQL) and distributed storage systems to access and manage EHR data.

  · Partner with data scientists and domain experts to design data pipelines that integrate with existing hospital systems.

  · Write clean, well-documented, and maintainable code following best practices

  · Contribute to shared code repositories using Git, ensuring reproducibility and version control for collaborative projects.

  · Develop CI/CD workflows to automate model testing, containerization, and deployment to production environments.

  · Monitor deployed models for performance drift, latency, and reliability, and implement automated alerts and feedback loops to refine model behavior.

  · Produce clear technical documentation, including system architecture diagrams, training procedures, and user guides for internal stakeholders.

  · Present engineering best practices, findings, and process updates to clinicians, researchers, and other non-technical audiences as needed.

Education Qualifications

• Bachelor's Degree Required

• Bachelor's Degree Analytics, Data Science, Statistics, Mathematics, Computer Science or a related field Preferred

• Masters or PhD in Analytics, Data Science, Statistics, Mathematics, Computer Science or a related field Preferred

Experience Qualifications