The Department of Biomedical Engineering - Prof. Elham Azizi's Lab, is looking for a Staff Associate III to:
Functional Knowledge (35%) – Develop advanced probabilistic and causal generative frameworks linking molecular to clinical data; design interpretable representation spaces for tumor-immune dynamics and therapy response.
Technical Expertise (25%) – Engineer LLM and diffusion/flow-matching pipelines; integrate multi-GPU distributed training, experiment tracking, and MLOps automation.
Problem-Solving Skills (15%) – Innovate adaptive fine-tuning, multi-task learning, and agentic reasoning strategies to improve generalization and data efficiency.
Decision Making/Autonomy (10%) – Lead architectural and data design decisions; prioritize experiments aligned with program milestones; evaluate trade-offs between model accuracy and interpretability.
Communication Skills (10%) – Present findings at internal symposia and external conferences; co-author manuscripts; write high-quality documentation and technical reports.
Mentorship/Leadership (5%) – Mentor junior engineers and research assistants; provide guidance on causal inference methods and AI-in-loop experimentation.
B.S./B.E. (minimum) in Computer Science, Biomedical/Electrical Engineering, Statistics, Bioinformatics, Applied Math, or related field. MS or PhD degree is preferred but not required.
Required qualifications
• Substantial expertise in training deep learning models and tuning large foundation models.
• Expertise with developing efficient data loaders for large datasets and optimizing training workflows.
• Deep knowledge of probabilistic modelling, self-supervised learning and representation learning, diffusion/VAE/flow matching architectures
• Strong Python, PyTorch/JAX, containerization & MLOps skills; familiarity with distributed training and modern experiment-tracking stacks
• Experience with AI coding tools (e.g., Copilot, Cursor)
Preferred extras
• M.S. or graduate-level degree in relevant field
• Experience with single-cell and spatial genomic or imaging data, and multimodal integration
• Expertise in statistical causal discovery and inference
• Publications or open-source contributions in generative models
• Strong interest in applications and driving impact in cancer biology and immunology
6+ years of experience in software engineering including:
3+ yrs hands-on experience training generative AI or large-language models at scale
2+ years experience with statistical or generative modeling
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