Cytokinetics is dedicated to advancing the frontiers of technology through the strategic application of artificial intelligence, machine learning, image analysis, and computational biology and chemistry. We are at the forefront of innovation, driving impactful solutions in drug discovery. As we expand our team, we are seeking a highly skilled Senior AI Expert to contribute to our ambitious projects and initiatives.

We are seeking a dynamic experienced AI Expert with a Ph.D in Machine Learning, Artificial Intelligence, Computer Science, or a related field. The candidate may have an accompanying degree in Bioinformatics, or a relevant discipline. This role requires extensive experience in building supervised learning models, particularly with regression and classification models for predictors and a deep understanding of biological data analysis. Experience of ML utilization for classification, regression, and clustering of highly complex metabolomic, proteomics, and flow cytometry / FACS data is also desired. Additionally, understanding and applying LLM models with integration with RAG model to scientific questions is also highly desired.

Responsibilities

• Collaborate closely with the Director of Discovery Technologies to foster and grow AI implementation, infrastructure, and processes within the organization.
• Lead the research, development, and implementation of advanced machine learning and deep learning algorithms to solve complex problems within Biological Sciences. This includes supervised, non-supervised analysis & visualization, and may occasionally involve image analysis, particularly in phenotypic screening, images of cells, and cell parts.
• Utilize regression models for predictors in biological data analysis to derive meaningful insights and predictions from large-scale datasets.
• Major contributor towards developing new use cases utilizing LLM (w/ RAG model) AI technology contributing directly towards drug discovery Program goals.
• Collaborate closely with interdisciplinary teams to define project objectives, design experimental frameworks, and deliver innovative solutions that drive impact across Research Discovery programs.
• Conduct thorough data preprocessing, feature extraction, and dimensionality reduction to optimize model performance and scalability.
• Stay abreast of the latest developments in AI, machine learning, bioinformatics, and cheminformatics, and integrate emerging technologies and methodologies into our workflows.
• Provide technical leadership and mentorship to colleagues, fostering a collaborative and intellectually stimulating environment.
• Evaluate and compare different machine learning frameworks and libraries to identify the most suitable tools for specific tasks.
• Contribute and assist to the development and documentation of best practices, standards, and guidelines for AI and machine learning within the organization.
• Communicate findings and results effectively to both technical and non-technical stakeholders through presentations, reports, and documentation.
• Take ownership of projects from conception to deployment, ensuring high-quality and reliable solutions are delivered on time and within budget constraints.
• Report regularly to the Director of Discovery Technologies on project progress, challenges, and opportunities for improvement, providing strategic insights and recommendations for further advancements.

Qualifications

• Ph.D. in Machine Learning, Artificial Intelligence, Computer Science, Bioinformatics, or relevant discipline
• Experience of 5 years of which may include post-doc in machine learning and AI field utilizing large-scale datasets and cutting-edge machine learning techniques to develop accurate and robust supervised learning models
• Proven expertise in building supervised learning models, with a focus on regression and classification of biological data analysis, including proteomics, metabolomics, and flow cytometry / FACS.Strong background in statistical analysis, data visualization, and algorithm development, with a demonstrated ability to tackle complex problems in interdisciplinary domains.
• Proficiency in programming languages such as Python, R, and experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Excellent communication skills, with the ability to convey technical concepts effectively to diverse audiences, both verbally, written, and presentation style.
• Proactive and results-driven approach

Nice-to-Haves:

• Experience with cloud-based machine learning platforms such as Amazon SageMaker or Apache Spark-based solutions like Databricks is advantageous.
• Experience using AWS Bedrock with RAG model and Graph Database integration.
• Familiarity with other Amazon Cloud AWS services