About the RoleWe are seeking research scientists and engineers in machine learning and artificial intelligence, with experience applying AI to biological problems or an interest in doing so. You will help lead the design, evaluation, and deployment of our multimodal genome language models, with a particular focus on our biosecurity and biodefense efforts.
In this role, you will collaborate with our multidisciplinary team (e.g., computational biologists, AI scientists, and systems architecture engineers) to translate advances in large-scale machine learning into capabilities for modeling biological systems. You will extend and adapt large model backbones-such as sequence and multimodal foundation models-to enable tasks across genomics, protein biology, and cellular systems. This includes designing
post-training pipelines, domain adaptation strategies, and evaluation frameworks that enable state-of-the-art ML frameworks to reason over biological data.
What You'll Do- Build and adapt frontier AI models through fine-tuning and post-training.
- Design and run experiments applying large models to problems relevant to biosecurity and biodefense.
- Bridge to real-world biosecurity systems. Translate technical capabilities into deployable systems such as early warning platforms, sequence screening tools, attribution technologies, and rapid detection and response pipelines. Work with government and partners to align systems with real-world workflows.
- Analyze model behavior and run ablations to understand model reasoning and failure modes in biological contexts.
- Explore modern mechanistic interpretability pipelines and methods for sequence characterization and model understanding.
- Prototype new approaches for prediction and design using foundation models.
- Operate with high ownership in a fast-moving, ambiguous environment.
What We're Looking For- Strong technical background in deep learning for large-scale classification, retrieval, or generative models.
- Ability to design evaluation tasks and benchmarks that measure 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)
- 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, or an interest in applying your AI/ML skills in this space.
- Ability to reason concretely about what biological AI systems can and cannot do, how those capabilities translate into real-world risks, and how to measure and mitigate those risks.
- High agency: defines problems, runs with them, and ships.
Nice to Have- Familiarity with multimodal biological modeling, including in genomics or protein structure.
- 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.
- Experience in applied areas of biosecurity or biodefense, such as pathogen-agnostic detection or countermeasure development.
- Familiarity with bioinformatics algorithms and tooling like BLAST, DIAMOND, HMMER, etc.
- Experience with models like biological foundation models like Evo, ESM, AlphaFold.
Why Radical Numerics- 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.
- Competitive compensation, comprehensive benefits, and support for continual learning.
