Join a cutting-edge team driving AI innovation in healthcare. As a Machine Learning Engineer, you’ll develop groundbreaking models that transform drug development and clinical trials. Collaborate with top researchers, work on high-impact projects, and contribute to scientific publications. If you’re passionate about using AI to solve complex healthcare challenges, apply today to make a real-world impact.

Pay: $$40-$50/hr

Key Responsibilities:

• Develop Machine Learning Algorithms: Design and implement innovative models to analyze and understand the relationships between complex biological data, including imaging and omics data.
• Collaborate Cross-Functionally: Work with machine learning scientists, imaging experts, and computational biologists to integrate machine learning solutions into clinical research and decision-making.
• Data Analysis and Insight Generation: Analyze large-scale biological and clinical datasets to extract actionable insights that influence drug development strategies and clinical trial designs.
• Stay at the Forefront of AI Research: Continuously explore and apply the latest advancements in machine learning to healthcare, ensuring that the team remains at the cutting edge of AI technology.
• Contribute to Scientific Publications: Publish your findings in leading scientific journals and conferences, contributing to the wider scientific community.

Qualifications:

• Education: M.S. or higher in Computer Science, Machine Learning, Statistics, Mathematics, Bioinformatics, or a related quantitative field.
• Experience: Proven experience in developing and applying advanced machine learning models in academic, research, or industry settings.
• Technical Expertise:
  • Proficiency in programming languages, particularly Python, and experience with machine learning frameworks such as JAX, PyTorch, or TensorFlow.
  • Familiarity with MLOps workflows, including version control systems, high-performance computing infrastructures, and machine learning experiment monitoring tools.
  • Experience building and deploying machine learning pipelines for scientific applications.
• Soft Skills: Strong collaboration and communication skills with a problem-solving mindset, capable of working across teams to achieve shared goals.

Preferred Qualifications:

• Experience working with multimodal data, including genomic, transcriptomic, and imaging data.
• Knowledge of multivariate analysis techniques such as GWAS and methods of integrating multimodal datasets.
• Experience with image-based representation learning and other data fusion techniques.

INDBH