Senior Computational Scientist (Single-Cell + Machine Learning)

We are searching for a Senior Computational Scientist with deep, hands-on single-cell expertise to help translate cutting-edge AI models into impactful discovery and translational workflows. This role sits at the intersection of single-cell biology, machine learning, and applied computational research, with direct influence on how next-generation foundation models are evaluated and deployed.

You will partner closely with research scientists, ML engineers, product teams, and external collaborators to turn advanced single-cell methods into actionable insights for real-world drug discovery and translational use cases. This is a senior IC position with the opportunity to grow into management as the team scales.

What You’ll Do

• Lead scientific strategy and execution for evaluating and applying foundation models to single-cell and multi-omics datasets.
• Develop, adapt, and benchmark single-cell workflows, including integration, perturb-seq analysis, and advanced multi-modal pipelines.
• Collaborate with research and ML teams to design experiments and rapidly iterate from prototype to insight.
• Work closely with Product and Business Development to translate complex biology + ML capabilities into customer-facing deliverables.
• Mentor junior scientists and contribute to shaping the culture and scientific standards of a growing computational biology team.
• Bring strong scientific judgment to ensure modeling work remains grounded in translational biology and drug development needs.

What We’re Looking For

• PhD in Computational Biology, Bioinformatics, Machine Learning, or a related field with a strong single-cell focus, and 2+ years of industry experience; or MSc with 5+ years of relevant industry experience.
• Deep, hands-on experience with single-cell technologies (integration, perturb-seq, multiome, advanced workflows).
• Strong understanding of modern ML/AI approaches for biological data, such as transformers, VAEs, diffusion models, or classical ML methods.
• Proficiency with ML frameworks like PyTorch (or equivalent).
• Excellent communication skills with the ability to explain complex science to diverse technical and non-technical audiences.
• Comfortable working in a fast-paced, iterative environment.
• Passionate about building at the intersection of biology, machine learning, and real-world translational impact.