Machine Learning Engineer

About the Role

Join our leading-edge machine learning team as a Research Engineer, and play a pivotal role in pioneering machine learning solutions for genetic data interpretation. Our team is a technically proficient Research Engineering team who can handle high computational complexity. This role requires significant expertise in model optimization, computational efficiency, and engineering fundamentals to support a DNA-based model infrastructure.

Responsibilities:

• Improve data loading and storage efficiency for high-volume datasets
• Manage large-scale genetic models
• Optimize multi-GPU performance
• Identify and resolve bottlenecks affecting model training and inference speeds
• Develop standards for software engineering practices within the team
• Collaborate on genetic target prediction by integrating human genetics and disease data

Requirements

• ⁠Experience running large models on extensive compute resources
• Proficiency in optimizing multi-GPU training and handling bottlenecks in high-complexity systems
• ⁠Deep understanding of computational complexity and efficiency in data engineering, particularly with terabyte-scale datasets
• ⁠Familiarity with tools like OpenVINO and ONNX for inference optimization
• Strong grasp of foundational engineering principles and software engineering best practices for maintaining scalable, efficient code

Key Technical Experiences:

• ⁠Multi-GPU optimization and troubleshooting
• Practical experience with fine-tuning and pre-training large language models or similar
• Ability to manage large data pipelines (terabyte scale) and enhance model responsiveness
• ⁠Skilled in handling data models directly, not NLP-focused LLMs