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.