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