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 with 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