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About the Role

Seeking research scientists and engineers at the intersection of machine learning and biological modeling to 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, including designing post-training pipelines, domain adaptation strategies, and evaluation frameworks.

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.
• 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.

Requirements

• 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.
• 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.

Skills

• Machine learning
• Deep learning
• Large-scale generative architectures
• Autoregressive LLMs
• Diffusion models
• Fine-tuning
• Post-training
• Adapters
• Architectural modifications
• ML models for biological data
• Genomics
• Gene regulation
• Protein biology
• Cellular systems
• Python
• PyTorch
• JAX
• Experiment management
• Distributed training
• Genomic sequence models
• Molecular sequence models
• Structural biology
• Multimodal biological modeling
• ML experiment scaling

Experience Level

• Mid-to-senior level

About the Company

• Radical Numerics is an AI research lab building general biological intelligence.
• Our mission is to master the code of life, and our purpose is to reduce human suffering.
• Our team created Evo, and started the field of generative genomics.
• Our work was featured on the cover of Science, and presented by our CEO on the main stage of TED2025.
• Evo was used to create the first AI gene therapy tool CRISPR-Cas9, and the first AI whole genome from scratch.
• Evo 2, featured in Nature, is the largest fully open source AI project across any domain.
• Radical Numerics is bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology.
• We’ve redesigned the foundation model training stack to turn the world’s raw scientific data (e.g. biological sequences, experiments, and physical processes), into intelligible, generative models that can expand and accelerate what humanity can understand, design, and cure.
• The same generative breakthroughs that enable life-saving cures also lowers the barrier to creating engineered threats and AI-generated bioweapons.
• We believe these forces are inseparable.
• Radical Numerics was founded to develop both the power to design and the responsibility to defend.
• We believe biology is the most impactful and consequential application of AI.
• Join peers that are mission driven, and dedicated to creating radically innovative tech that will change the world and human health for the better.
• Work on frontier AI systems in a collaborative culture that values rigor, creativity, and cross-disciplinary partnership across AI labs, biotechs, hospital systems, and national research institutes.

Equal Opportunity

• Radical Numerics is committed to equal employment opportunity and does not discriminate in any employment opportunities or practices based on an individual's race, color, creed, gender (including gender identity and gender expression), religion (all aspects of religious beliefs, observance or practice, including religious dress or grooming practices), marital status, registered domestic partner status, age, national origin or ancestry (including language use restrictions and possession of a driver’s license issued under California Vehicle Code section 12801.9), natural hair, physical or mental disability, political affiliation, medical condition (including cancer or a record or history of cancer, and genetic characteristics), sex (including pregnancy, childbirth, breastfeeding or related medical condition), genetic information, sexual orientation, military and veteran status or any other consideration made unlawful by federal, state, or local laws.
• It also prohibits unlawful discrimination based on the perception that anyone has any of those characteristics, or is associated with a person who has or is perceived as having any of those characteristics.