to design and build a scalable, secure, and modular platform that powers AI research in healthcare. You'll lead the technical architecture for training and deploying models on complex biomedical data, helping shape the future of AI in life sciences.
Key Responsibilities
Design and build a scalable AI/ML platform to support data pipelines, model training, and deployment.
Work with large, diverse biomedical datasets (clinical, genomic, proteomic, chemical).
Build secure, cloud-native infrastructure using containers and APIs.
Implement and scale foundation models, knowledge graphs, and embeddings.
Ensure compliance with security and privacy standards (e.g., HIPAA, SOC2).
Collaborate with cross-functional teams (data scientists, clinicians, engineers).
Mentor engineers and set best practices for ML platforms and MLOps.
Required Skills & Experience
Master's or PhD in Computer Science, AI/ML, or related field.
10+ years in AI platform or infrastructure roles.
Strong experience with Python, ML frameworks (PyTorch, TensorFlow), and cloud platforms (AWS, GCP, Azure).
Experience with distributed systems, MLOps, and tools like MLFlow, Kubeflow, Databricks.
Familiar with GPUs, performance optimization, and data security practices.
Nice to Have
Background in life sciences or biomedical data (genomics, proteomics, EHRs).
Familiarity with drug discovery workflows and generative AI tools like LangChain or Hugging Face.
Knowledge of bioinformatics databases and ontologies.
What We Offer
Chance to shape a cutting-edge AI platform in a mission-driven startup.
Equity and growth opportunities in a fast-moving team.
* Budget for learning and experimentation with AI/cloud tools.
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