AI Scientist

Noetik

4h•Hybrid

About The Position

We’re looking for an AI scientist to lead the development, training, and evaluation of custom world models of patient cancer biology. The models will be built using NOETIK’s extensive pan-cancer multimodal spatial dataset – the largest of its kind – combined with other datasets and modalities, and will be used for drug development, clinical trial design, and deep biological insight. There is no blueprint for how to build models that make the most of this data; we’re looking for somebody who is excited to explore, experiment, and forge this field with us by developing new architectures, loss functions, and evals. The AI team at Noetik operates as a fast-paced research lab emphasizing creativity and urgency in our work. We believe that the fastest way to make an impact for patients is to pair big ideas with rigorous science. This is fundamental research applied to real problems: the breakthroughs you make in your research will translate directly to improved care for patients. In this role you will be responsible for the development and evaluation of Noetik's custom models. You will be empowered to pursue a research agenda that is supported by substantial GPU allocation per researcher, work alongside a data-generating lab on “lab in the loop” iterations for model exploration and improvement, and collaborate with a world-class team of engineers and researchers.

Requirements

No prior experience in biology or oncology is required. We welcome applicants with backgrounds in computer vision, robotics, NLP, computational biology, neuroscience, and more. If you have experience obsessively checking loss curves, we want to hear from you.
Publications and presentations in leading journals and machine learning conferences (e.g. NeurIPS, ICLR, ICML, CVPR)
A PhD in a quantitative field using modern machine learning methods, or equivalent research experience
Experience writing model architecture code and datasets in PyTorch, training and optimizing models, and evaluating trained models
Deep knowledge of modern machine learning architectures and self-supervised learning approaches (autoencoders, diffusion, flow-matching, autoregressive decoders)
Familiarity with training and inference in large-scale distributed systems, including advanced parallelism approaches and supporting frameworks
Practical use of modern AI tools to accelerate scientific work without sacrificing integrity and rigor

Responsibilities

Design, implement, and train foundation models that integrate biology across spatial scales and measurement modalities
Scale model recipes to massive datasets and compute infrastructure
Contribute ideas in internal lab meetings and journal clubs, learn from the expertise on the team, and share your own
Identify meaningful benchmark tasks and evaluations for biology foundation models, and systematically evaluate open source alternatives on those benchmarks
Own research projects from conception to execution, communicate results to the broader team, make findings legible to biologists and partners, and collaborate with domain experts
Present your research at leading conferences in AI and biology
Rapidly iterate on research ideas, identify the highest leverage experiments to run, and make informed decisions with conviction

Benefits

Generous health, dental, and vision insurance, with premiums fully covered for both employees and their dependents (depending on your plan choice).
Modern care platforms like OneMedical, Midi Health, Spring Health, and XP Health.
HSA and FSA accounts, dependent care savings, an EAP, resources for mental health (Rula), and health care navigation and advocacy (Rightway).
Flexible PTO
Nine paid holidays
Two full-week company breaks (the first week of July and the last week of December), plus the Friday after Thanksgiving.
Competitive base salary
Stock options
401(k) plan with an employer match.
Fully-covered disability, life and AD&D insurance
Generous parental leave.
SoFi, Multiply Mortgage, and optional programs for pet insurance, legal services, and identity theft protection.