"Staff Associate III"

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

Columbia University is an Equal Opportunity Employer / Disability / Veteran

Pay Transparency Disclosure
The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above hiring range represents the University’s good faith and reasonable estimate of the range of possible compensation at the time of posting.