Member of Technical Staff, AI Bio
Hybrid (San Francisco Bay Area), Hybrid (Tokyo)Full-timePosted 7mo ago
About the role
Research scientist/engineer role at the intersection of machine learning and biology, focused on adapting and extending large-model backbones to genomics, protein biology, and cellular systems. Develop post-training pipelines, domain adaptation strategies, evaluation frameworks, and integrate biological capabilities into next-generation foundation models.
Responsibilities
- 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 such as variant interpretation, gene regulation modeling, protein function prediction, and multimodal cellular modeling — and drive these capabilities toward grounded downstream biological impact.
- Design biologically meaningful data representations and modeling schemes across sequence, molecular, and multimodal data modalities.
- Analyze model behavior and run ablations to understand model reasoning and failure modes in biological contexts.
- 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.
Qualifications
- Strong background and intuition in machine learning and deep learning across large-scale generative architectures, from autoregressive LLMs to diffusion models.
- 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 that measure biological model capability beyond simple accuracy metrics, with a critical eye toward aligning computational outputs with actionable downstream applications.
- 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.
Skills
Required:Machine learning / deep learningModel adaptation techniquesBiological data applicationsPythonPyTorch or JAX
Last updated May 28, 2026. Interested? Apply on the company site.
About Radical Numerics
Next-gen AI lab for recursive self-improvement
Radical Numerics - Next-gen AI lab for recursive self-improvement
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