Senior ML Research Engineer, Virtual Cell

About the role

The AI Sim R&D team builds cutting‑edge machine‑learning and physics‑based models (LQMs) to accelerate drug discovery. As a Senior ML Research Engineer on the AQCell virtual‑cell platform, you will develop and maintain the models and data infrastructure that predict transcriptomic responses and downstream functional endpoints for chemical perturbations.

Key responsibilities

• Model Development: Build, train, and maintain ML models for expression‑perturbation and functional‑endpoint prediction.
• Large‑Scale Dataset Management: Acquire, harmonize, and manage massive transcriptomic and drug‑response datasets such as LINCS L1000, GDSC, Tahoe‑100M, and DILImap.
• Evaluation & Baselines: Automate and harden the end‑to‑end evaluation pipeline, implementing robust statistical baselines.
• Research Translation: Convert state‑of‑the‑art scientific methods (e.g., transformer‑based perturbation models, knowledge‑graph embeddings) into production code.
• Cross‑Functional Collaboration: Work with computational biologists, software engineers, and product stakeholders to ensure models are biologically sound and usable.
• Communication: Document methods, assumptions, and results and present findings to technical and non‑technical audiences.

Required profile

• Academic foundation in a quantitative field (BSc, MSc or PhD preferred).
• Proven experience building and maintaining ML models in a scientific or life‑science setting.
• Hands‑on experience managing large‑scale biological datasets and training pipelines.
• Strong software engineering skills in Python and modern ML frameworks (PyTorch, JAX).
• Ability to design rigorous evaluation methodologies and interpret model performance against meaningful baselines.

Required skills

• Python programming.
• PyTorch and/or JAX.
• Experiment tracking and data versioning tools (e.g., Weights & Biases).
• Transcriptomics data harmonization and normalization (batch correction, pseudobulking, gene ID mapping).
• Knowledge‑graph embeddings and graph neural networks applied to drugs, targets, or cells.
• Cheminformatics representations such as SMILES and InChI keys.

What we offer

• Competitive base salary, performance‑based incentives and equity participation.
• Comprehensive medical, dental and vision coverage for employees and dependents.
• Retirement savings plan with company matching.
• Paid parental leave and inclusive family‑building benefits.
• Flexible paid time off, seasonal breaks and support for remote or hybrid work arrangements.
• Continuous learning opportunities through internal development programs and cross‑functional projects.