Lead Data Engineer

Key Responsibilities:

• Lead the strategic design of a holistic solution to our large and diverse data usage needs,
• Set the collaboration and reusability strategy for data consumption including publicly available and partner generated data
• Ensure the FAIR principles are followed  in our data storage and retrieval strategy.
• Build and maintain scalable, efficient, and reusable data products and codebases for large-scale foundation model training, adaptation, evaluation, and inference.
• Collaborate closely with data engineers and research scientists to integrate models into production environments.
• Ensure code quality, scalability, and performance through rigorous testing and code reviews.

Qualifications:

• Bachelor’s, Master’s degree in Computer Science, Engineering, or related field. Experience in life sciences or healthcare is required.
• Strong familiarity with at least some (the more the better) of the following biomedical data types: Sequencing data, other high throughput omics data, biological imaging data, clinical and phenotypic data
• Experience with using (developing an advantage) large scale data products and systems for biological or biomedical applications.
• Stong programming skills in JavaScript, Python, and modern web development frameworks, and familiarity with GPU-accelerated tools (e.g., CUDA, cuDNN, Triton).
• Knowledge of major deep learning frameworks such as PyTorch, HuggingFace Transformers & Accelerate, or Megatron-LM/DeepSpeed.
• Familiarity with resource management and scheduling systems (e.g., SLURM, Kubernetes).
• Proficiency in back-end frameworks like Django, Flask, or Node.js, and database technologies (e.g., PostgreSQL, MongoDB).
• Expertise in distributed systems, cloud computing (AWS, GCP), and containerization tools (Docker, Kubernetes).

Preferred Qualifications:

• Prior experience pre-training or serving large language models or large-scale foundation models.
• Experience with deep learning workflows.
• Knowledge of challenges and experience with bioinformatics tools
• Familiarity with version control systems like Git and CI/CD pipelines.
• Strong understanding of RESTful APIs, authentication, and deployment pipelines
• Familiarity with machine learning workflows and biological datasets.