Sr. AI Scientist, Computational Biology

About QuantumCell

At QuantumCell, we are rewriting the rules of drug discovery — and we're just getting started. We are a dynamic tech company in the biology space on a mission to decode and defeat the most complex brain disorders of our time. By converging next-generation sensing technologies, autonomous robotics, and cutting-edge AI, we are building the world's first high-fidelity virtual cell and virtual system model: a mechanistically anchored simulation of human biology that can predict how drugs will behave in a patient before a single trial is run.

Anchored by world-class scientific partnerships and access to elite computational infrastructure, we generate new types of biological data at a scale and resolution that no one has achieved before. This isn't incremental science. It's a fundamental shift from reductionist biology to a true systems-level understanding of the human brain — and we are looking for bold, brilliant people to help us build it!

The Role

We are seeking a Senior Computational Biologist to join our Virtual Lab team as a technical lead for the training and optimization of our large-scale virtual cell and real-world evidence foundation models. Collaborating with the AI team on the model architectures for QuantumCell’s unique, biological data, you own the translation of that direction into trained, optimized, and biologically validated models — and you lead the team that executes it.

This is a senior individual contributor and team leadership role. You will set the standard for how QuantumCell trains and evaluates virtual cell models, mentor junior computational biologists, and serve as the primary scientific bridge between our AI and experimental biology teams. You will be the expert who ensures our models don't just perform well statistically — but that they learn real biology.

What You Will Do

Technical Leadership & Strategy

* Define and own the end-to-end model training strategy for QuantumCell's virtual cell foundation models, from pre-training through fine-tuning and domain adaptation.

* Lead design reviews for training pipelines, data representation strategies, and biological evaluation frameworks in close collaboration with the AI Scientist.

* Mentor and technically guide junior computational biologists, establishing best practices for reproducible, rigorous model training and biological validation.

* Contribute to scientific strategy discussions at the leadership level, translating the state of the field into actionable direction for QuantumCell's modeling program.

Model Training & Optimization

* Own the full training lifecycle across single-cell, multi-omic, and perturbation datasets, including hyperparameter optimization, scaling experiments, and training stability improvements.

* Diagnose and resolve complex training failure modes — loss landscape pathologies, distributional shift, data imbalance — and develop principled, generalizable solutions.

* Evaluate model checkpoints against biologically meaningful benchmarks, not just loss metrics, and drive iterative improvement cycles.

* Collaborate with ML Ops to build and maintain robust, scalable training infrastructure on large GPU clusters.

Biological Data Curation & Representation

* Lead the curation, harmonization, and quality control of large-scale biological training datasets across transcriptomics, proteomics, epigenomics, and spatial modalities.

* Design biologically informed tokenization and data representation strategies tailored to each modality's structure and semantics.

* Collaborate with experimental teams to integrate proprietary datasets into training pipelines, providing the biological context that shapes data weighting and representation.

Biological Validation & Benchmarking

* Develop and own a comprehensive biological validation framework — covering gene regulatory inference, perturbation response prediction, cell state classification, and drug response modeling — as the primary success standard for all model training.

* Critically interpret model outputs through a biological lens, distinguishing genuine learned biology from statistical artifacts.

* Partner with experimental biologists to design wet lab validation experiments that close the loop between model predictions and empirical biology.

Scientific Communication & Collaboration

* Serve as the primary liaison between the AI team and experimental biology team, translating biological questions into training objectives and communicating model capabilities and limitations back to scientists.

* Lead and contribute to publications, patents, and conference presentations representing QuantumCell's virtual cell research.

* Maintain rigorous documentation of training decisions, dataset choices, and validation outcomes to build institutional knowledge.

What You Will Bring

Required

* Ph.D. in Computational Biology, Bioinformatics, Systems Biology, or a closely related field, plus 4+ years of postdoctoral or industry experience.

* Deep expertise in single-cell genomics, including tools such as Scanpy, Seurat, and related frameworks.

* Demonstrated experience training and optimizing large-scale foundation models on biological data, with evidence of independently leading such efforts.

* Strong proficiency in Python and PyTorch; fluency with large-scale ML training infrastructure and distributed compute.

* Proven ability to evaluate models using biologically grounded criteria, not just standard ML benchmarks.

* Comprehensive understanding of CNS-relevant biology: cell types, gene regulatory programs, signaling pathways, and disease-relevant perturbations.

* Experience mentoring junior scientists or leading small technical teams.

* Strong scientific communication skills across both ML and biology audiences.

Preferred

* Experience training or fine-tuning published virtual cell or single-cell foundation models (e.g., scGPT, Geneformer, scFoundation, or similar).

* Multi-omic data integration experience across transcriptomics, proteomics, epigenomics, or spatial modalities.

* Experience with single-cell perturbation datasets (CRISPR screens, small molecule screens) in a model training context.

* Prior experience in a research-stage biotech or drug discovery environment.

* Track record of first- or co-author publications at top-tier computational biology or ML venues.

What We Offer

* Competitive compensation package (salary, equity, bonus) based on experience.

* Comprehensive medical, dental, and vision benefits.

* Four weeks of paid time off.

* Access to world-class scientific collaborators and leading computational infrastructure, including large-scale GPU clusters and proprietary biological datasets of exceptional scale and resolution.

* A unique opportunity to help define how AI learns from biology at one of the most ambitious companies in CNS drug discovery.

* The chance to contribute to a deeply mission-driven organization focused on transforming patient outcomes and advancing treatments for complex diseases.

* Support for conference attendance and active participation in the scientific community.

* A collaborative, inclusive, and fun culture where you’ll play a key role in building the team, shaping the environment, and driving meaningful impact from the ground up.

How to Apply

Send your CV to: [email protected]

We are an equal opportunity employer.