Machine Learning Engineer

Roles and responsibilities

We are seeking an experienced Machine Learning Engineer-GPU with a focus on large-scale deep learning to join our dynamic team dedicated to pioneering the development and application of foundation models in biomedical research. The ideal candidate will drive the implementation of advanced machine learning methods, particularly in the areas of foundation models, representation learning, large language models, and generative AI. You will be involved in a variety of internal and external projects, contributing to methodological research that unlocks novel applications in collaboration with our research scientists. Depending on your level of expertise, you may also have the opportunity to supervise a team and lead ambitious projects.

Key Responsibilities

• Foundation Model Development: Collaborate with a team to establish foundational models that advance biomedical research.
• Implementation of Advanced Methods: Drive the application of cutting-edge machine learning techniques, with an emphasis on representation learning, large language models, and generative AI.
• Methodological Research: Engage in research that identifies innovative applications of machine learning in the biomedical field, working closely with research scientists to translate theoretical concepts into practical solutions.
• Team Leadership: Depending on your experience, lead projects and mentor junior team members to foster a collaborative and high-performing team environment.

Desired candidate profile

Qualifications

The ideal candidate will embody a 'team-first' attitude, demonstrating independence, curiosity, and meticulous attention to detail. You should thrive in a fast-paced, dynamic environment and contribute positively to the team culture.

• Technical Expertise:

  • Proven experience in crafting high-performance CUDA kernels to maximize GPU efficiency.
  • In-depth knowledge of distributed computing frameworks for managing contemporary GPU clusters.
  • Expertise in optimizing performance from modern GPUs (e.g., H100) for efficient model training and inference through low-level coding and architectural redesign.
  • Strong grasp of generative AI, with practical experience in fine-tuning and deploying large neural networks in industrial settings.
  • Proficiency in deep learning frameworks such as TensorFlow, PyTorch, or JAX, with mastery of at least one framework.