Overview In this role, you apply advanced machine learning to large-scale perturbation data to aid target and drug discovery. You will work within the Perturbation Biology group in gRED and collaborate across teams to translate data into actionable insights. You'll design predictive ML models, integrate diverse data modalities, and scale methods for big datasets. You will contribute to high-impact research, publish in top venues, and help advance Roche's mission to improve patient outcomes.
Compensation / Benefits- discretionary annual bonus
- salary range disclosed
- relocation not offered
- comprehensive benefits (as per company policy)
- opportunity to publish in top venues
- career development in a leading biotech environment
Responsibilities- Design and apply predictive ML algorithms for lab-in-the-loop perturbation screens for drug and target identification
- Integrate diverse data modalities including molecular structures, omics, images, and text
- Collaborate with biologists, chemists, data scientists, and stakeholders
- Build and scale ML techniques for massive datasets and support deployment of novel algorithms
- Publish and present results at top-tier ML venues and scientific events
Key requirements- PhD in a quantitative field or physical/life sciences with strong quantitative focus
- 0-2 years post-PhD for Senior ML Scientist; 2-7 years post-PhD for Principal ML Scientist
- Proven track record in developing/applying advanced ML models in research or industry
- Interest in biology and chemistry as applied to disease treatment discovery and development
- Excellent communication
- Collaborative mindset
- Problem-solving aptitude
- Proficient in Python
- Experience with PyTorch, JAX, TensorFlow
- Strong background in statistics, probabilistic modeling and data analysis
