Sr Machine Learning Engineer

SR. MACHINE LEARNING ENGINEER

SAN FRANCISCO, CA (Hybrid)

$200,000 - $290,000 Salary

Company:

Our client is an AI- Native biotechnology company focused on harnessing machine learning to solve complex challenges in healthcare. By combining advanced AI techniques with cutting-edge research, they aim to develop innovative solutions that transform the landscape of medicine.

The Role:

As a Sr. MLE, you'll work with a highly technical, interdisciplinary team to design and scale systems that support the research and development of transformative therapies. This role will have a focus on optimizing infrastructure and systems for scalable training and deployment of ML models.

Key Responsibilities:
•  Design, build, and maintain distributed systems for training and inference of machine learning models at scale (e.g., vision transformers).
•  Manage GPU clusters and cloud infrastructure, ensuring efficiency and scalability for large-scale workloads.
•  Collaborate with ML and Engineering teams to implement an ML Platform that streamlines both research iteration and scaling.
•  Optimize model architectures, data loaders, and training pipelines for performance and efficiency.
•  Develop systems for effective analysis of model results and scalable deployment solutions. Qualifications:
•  Proven experience building and scaling distributed systems for ML training and inference
•  Experience working with Large GPU Clusters
•   AWS
•  Strong proficiency in PyTorch
•  Experience with ML frameworks
•  Deep understanding of cloud computing platforms, distributed systems, and scalable infrastructure.
•  Strong Communicator
•   Nice-to-have's:
•  Ray Framework
•  Kubernetes
•  Sagemaker
•  Optimization of data loaders
•  Experience working with multiple data modalities (e.g., images, sequences)
•  Built custom data pipelines
•  Experience deploying production software If you're interested please click apply. If you're REALLY interested - please email your current resume and the following information:

•  Current location
•  Years of Experience
•  Tools/models you work with
•  How your experience compares to role qualifications
•  Your availability for a quick introductory call