Senior ML Research Engineer, Virtual Cell
SandboxAQ · Remote · United States ·
COMPENSATION
$168k – $252k
Listed on the employer's job board (Ashby)
- Senior level role
- Fully remote
- Python + PyTorch/JAX
- Drug discovery focus
- Equity participation
Join the AI Sim R&D team to develop Large Quantitative Models that advance drug discovery. You will work on AQCell, a virtual cell platform that predicts biological repercussions of compounds by mapping transcriptomic responses to functional endpoints like cell viability and toxicity.
This position sits at the intersection of machine learning and biology. You will build data infrastructure, train prediction models on large heterogeneous datasets, and harden evaluation pipelines to ensure reliable model performance for real-world scientific decisions.
What you'll do
- Build and maintain ML models for expression-perturbation and functional-endpoint prediction
- Acquire, harmonize, and manage large-scale biological datasets like LINCS L1000 and GDSC
- Automate and strengthen end-to-end evaluation pipelines with robust statistical baselines
- Translate scientific literature concepts into tested code within the modeling framework
- Collaborate with computational biologists and engineers to ground models in biology
- Document methods and communicate results to technical and non-technical stakeholders
What you'll need
- Bachelor's degree in Computer Science, Physics, Math, Biology, or related field
- Experience building and maintaining ML models in a scientific industry setting
- Proven ability to manage large-scale datasets and model training pipelines
- Strong Python skills with experience in PyTorch, JAX, or similar frameworks
- Ability to design sound evaluation methodologies and interpret model performance
Nice to have
- Advanced degree (MS or PhD) in a relevant quantitative or scientific field
- Experience with transcriptomics harmonization and normalization tools
- Familiarity with public perturbation datasets like DepMap or single-cell atlases
- Knowledge of graph neural networks or knowledge graph embeddings
- Understanding of cheminformatics representations such as SMILES or InChI keys
Stack
- Python
- PyTorch
- JAX
- Weights & Biases
How you'll work
- Fully remote work arrangement
- Flexible work arrangements supported
- Global team environment
What you get
- Competitive base salary and equity participation
- Performance-based incentives or bonuses
- Comprehensive medical, dental, and vision coverage
- Retirement savings with company matching
- Paid parental leave and family-building benefits
- Flexible paid time off and seasonal breaks
- Access to internal learning and development programs