About the Role
The candidate will develop and optimize infrastructure that supports large-scale machine learning workflows, ensuring reliability, scalability, and performance across research and production environments.
Responsibilities
- Design and build systems to support training and deployment of machine learning models
- Improve scalability and efficiency of distributed computing environments
- Collaborate with data scientists and researchers to understand infrastructure needs
- Implement tools for monitoring, logging, and debugging ML pipelines
- Optimize data processing workflows for speed and reliability
- Maintain and enhance internal platforms for model experimentation
- Ensure infrastructure aligns with security and compliance standards
- Support the deployment of models into clinical and research settings
- Troubleshoot performance bottlenecks in ML training jobs
- Contribute to documentation and best practices for engineering teams
- Work closely with software engineers to integrate ML systems into broader platforms
- Evaluate and integrate new technologies to improve infrastructure capabilities
- Automate repetitive tasks in model training and evaluation pipelines
- Assist in capacity planning for compute and storage resources
- Ensure system reliability during high-throughput processing periods
- Participate in code reviews and system design discussions
- Develop APIs and interfaces for internal ML tools
- Support reproducibility and versioning of ML experiments
- Help maintain continuous integration and delivery pipelines
- Contribute to incident response and system recovery processes
Compensation
Competitive salary and benefits package
Work Arrangement
Hybrid work model available
Team
Collaborative team focused on advancing early cancer detection
About the Role
This position plays a key role in enabling machine learning at scale by building robust, efficient infrastructure. The engineer will work on systems that process large biological datasets and support the development of models aimed at detecting cancer in early stages.
What We’re Looking For
We seek a technically strong engineer who is passionate about infrastructure and its impact on scientific discovery. The ideal candidate combines deep technical expertise with a collaborative approach and a commitment to quality.
