We’re looking for a Principal Computational Scientist/Director to help translate our foundation models into impactful, real-world discovery workflows. In this role, you’ll provide scientific leadership across model evaluation and applied use cases, ensuring our work stays grounded in the most relevant challenges in drug development and translational research.
You’ll join as a senior individual contributor with leadership and strategy experience at a point where the the team scales. You will act as a key scientific partner to our research team, mentor and support junior colleagues, and work closely with Product and Business Development to turn cutting-edge biology + ML into deliverables that matter for customers.
Requirements
- PhD in Computational Biology, Machine Learning, Bioinformatics, or a related field, with a strong focus on Transcriptomics OR Genomics + ML, and 2+ years of industry (non-academic) experience OR MSc in a relevant field and 5+ years of industry experience applying ML to Transcriptomics OR Genomics problems
- Strong understanding of ML/AI methods for biological data (e.g., transformers, VAEs, diffusion models, classical ML)
- Hands-on experience with modern ML frameworks such as PyTorch (or equivalent)
- Leadership experience with small teams to work towards defined roadmaps or projects
- Excellent communication skills — able to translate complex science into clear, actionable insights for technical and non-technical stakeholders
- Comfortable in a fast-paced, high-iteration environment, moving quickly from prototype → experiment → insight
- Strong passion for building at the intersection of biology and machine learning
Skills Required
- PhD in Computational Biology, Machine Learning, Bioinformatics, or a related field focused on early drug discovery and machine learning, plus 2 or more years of industry experience; or an MSc in a relevant field plus 5 or more years of industry experience applying machine learning to early drug discovery
- Strong understanding of machine learning and artificial intelligence methods for biological data, including transformers, variational autoencoders, diffusion models, and classical machine learning
- Hands-on experience with modern machine learning frameworks such as PyTorch or equivalent
- Leadership experience with small teams working toward defined roadmaps or projects
- Excellent communication skills, including translating complex scientific concepts into actionable insights for technical and non-technical stakeholders
- Ability to work in a fast-paced, high-iteration environment moving from prototype to experiment to insight
- Strong interest in applying biology and machine learning together
What We Do
The AI In-Silico Lab, powered by Bio Foundation Models — enabling virtual experiments for target ID, RNA design, and patient stratification.
