Our client’s team harnesses machine learning to advance drug development and optimize clinical trial design. Their work involves developing cutting-edge multimodal generative models, representation learning techniques, and reinforcement learning applications to extract meaningful insights from imaging and omics data.
Title: Machine Learning Engineer - Imaging and Omics
Job Type: Contract only through end of the year
Location: Onsite (South San Francisco, CA, US) or Remote (Must be available during PST)
Pay rate: $37-50/hr+ Depending on experience
About the Role
We are seeking a highly skilled and motivated Machine Learning Engineer to join a research-driven computational sciences team focused on developing novel machine learning methods for drug development and clinical trial design. The team works at the intersection of biology and AI, applying cutting-edge techniques such as multimodal generative models, representation learning, and reinforcement learning to improve healthcare outcomes.
As a key contributor to high-impact projects, you will have the opportunity to publish in top-tier conferences and journals while advancing machine learning models that drive scientific innovation in clinical research. The ideal candidate will have a strong foundation in machine learning, a passion for interdisciplinary research, and experience translating research ideas into real-world applications.
Responsibilities
- Design and implement novel machine learning algorithms to analyze relationships between imaging and omics data.
- Collaborate with cross-functional teams, including machine learning scientists, imaging experts, and computational biologists, to integrate ML solutions into disease research and clinical decision-making.
- Analyze complex biological and clinical data to generate insights that guide drug development and trial design.
- Stay informed about emerging trends in machine learning and their applications in healthcare and clinical trials.
- Contribute to scientific publications and present findings at relevant conferences.
Qualifications
Required:
- M.S. in Computer Science, Machine Learning, Statistics, Mathematics, Physics, Bioinformatics, Bioengineering, or a related quantitative field.
- Proven experience in developing and applying advanced ML models in research or industry settings.
- Proficiency in Python and experience with machine learning frameworks such as JAX, PyTorch, or TensorFlow.
- Familiarity with MLOps workflows, including code version control, high-performance computing, and machine learning experiment tracking.
- Ability to design and deploy ML pipelines for scientific analysis.
- Strong problem-solving, collaboration, and communication skills.
Preferred:
- Experience working with multimodal data, such as:
- Omics (e.g., genomics, transcriptomics), particularly in multivariate GWAS analysis.
- Imaging and image-based representation learning methods.
- Familiarity with multimodal data integration and cross-domain mapping strategies.
