Member of Technical Staff, AI Bio

The role

Research scientists and engineers will develop frontier AI architectures for biological problems. You will extend and adapt large model backbones to enable tasks across genomics, protein biology, and cellular systems.

What you will do

• Adapt frontier AI models to biological tasks through fine-tuning, post-training, adapters, and architectural modifications.
• Design and run experiments applying large models to problems in genomics, regulatory biology, protein biology, or cellular systems.
• Develop evaluation pipelines and benchmarks for biological tasks.
• Design biologically meaningful data representations and modeling schemes.
• Analyze model behavior and run ablations to understand model reasoning and failure modes.
• Explore modern mechanistic interpretability pipelines and methods for biological discovery.
• Collaborate with model architecture teams to integrate biological capabilities into next-generation foundation models.
• Prototype new approaches for biological prediction and design using foundation models.

Required qualifications

• Strong background and intuition in machine learning and deep learning across large-scale generative architectures.
• Experience adapting large models to new domains through fine-tuning, post-training, adapters, or architecture modifications.
• Experience applying ML models to biological data and challenging prediction tasks.
• Familiarity with molecular biology and biological data modalities, particularly genomics, gene regulation, protein biology, or cellular systems.
• Ability to design evaluation tasks and benchmarks.
• Strong Python and ML tooling experience (PyTorch or JAX, experiment management, distributed training).
• Ability to interpret biological datasets and translate biological questions into machine learning experiments.
• Mid-to-senior level experience building and deploying ML systems in research or production environments.

Nice to have

• Experience with genomic or molecular sequence models (e.g., Evo, HyenaDNA, AlphaFold-style models, AlphaGenome-style tasks, virtual cell models).
• Background in ML for structural biology, or (bio)chemistry.
• Familiarity with multimodal biological modeling, including transcriptomics, epigenomics, chromatin accessibility, or spatial biology.
• Experience building robust evaluation suites for large AI systems.
• Experience scaling ML experiments across large GPU clusters.
• Research publications in ML for biology, chemistry, or related areas.

About the company

Radical Numerics is an AI research lab building general biological intelligence. Their mission is to master the code of life and reduce human suffering.