Design and develop robust deep learning models for use in genomics, image processing, time-series analysis and genomics-adjacent applications.Develop and implement data processing pipelines for large-scale genomics datasets (, sequencing data, variant calling data, gene expression data).Optimize model performance for real-world scenarios, considering factors like data variability, noise, and computational efficiency.Collaborate with multi-functional teams to deploy scalable AI solutions and integrate AI models into customer-facing applications.Implement automated testing and monitoring systems to assess model quality and performance.Deploy and maintain ML models in production environments, ensuring scalability and reliability.Present technical findings and progress updates to team members and partners.
Education
BS in Computer Sciences, Mathematics, Statistics or a related field; a Master’s degree is highly preferred
Required Qualifications
3+ years of experience in developing and deploying machine learning models.Proficient in Python and relevant machine learning libraries (, TensorFlow, PyTorch, scikit-learn).Familiarity with data preprocessing and feature engineering techniques.Experience with basic ML infrastructure (, model deployment, model evaluation, optimization, data processing, debugging).Familiarity with version control systems like Git.Desired Qualifications
Master's or in a relevant field.5+ years of experience in developing and deploying machine learning models for genomics data analysis.Experience with containerization and orchestration tools (Docker, Kubernetes).Expert in Python.
Experience with C++/ Java is a plus.Experience with MLOps practices and tools.Knowledge of LLMs, LangChain, LlamaIndex, and RAG frameworks.