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:

1. Current location
2. Years of Experience
3. Tools/models you work with
4. How your experience compares to role qualifications
5. Your availability for a quick introductory call