We are seeking an AI data scientist to join our data analysis team and develop and operate Anima’s AI analysis pipelines.

Anima Biotech built the Biology Runtime Layer for AI - the Biology GPU - enabling AI models to visually compute inside cells and reason experimentally in biological pathways.

 The Biology GPU unlocks breakthrough applications across the R&D value chain - from discovering pathway signatures and identifying novel targets, to optimizing compounds based on pathway activity and supporting preclinical decisions through seeing biology inside cells.

 Our AI-to-cell stack powers use cases across the discovery process in over 20 programs and strategic collaborations with AbbVie, Takeda, and Lilly.

Key responsibilities:

• Develop and maintain the state-of-the-art Anima imaging AI models.

• Participate in the development of end-to-end data pipelines from raw images to structured datasets at scale, including preprocessing, segmentation, inference, and model scoring.

• Perform advanced analyses such as classification, clustering, anomaly detection, and other methods to derive actionable insights from complex imaging datasets.

• Participate in the development of Anima’s proprietary knowledge LLM.

• Collaborate with internal and external teams, including biologists and domain experts, to integrate AI and classical data analysis solutions.

• Identify and recommend new opportunities to expand and deepen AI adoption across the organization.

• Maintain documentation, coding best practices, and reproducibility of pipelines and models.

• Stay current with emerging trends in AI, deep learning, and imaging technologies.

Education, experience, and other requirements:

• MSc or PhD in Computer Science, Computer Engineering, or a related field, with foundational coursework in biology.

• 1-2 years of experience developing AI solutions, ideally in the domain of biological or medical imaging.

• Proven experience applying deep learning to extract insights from images.

• Experience with self-supervised or advanced generative models.

• Experience working with LLM, fine-tuning, RAG, etc. 

• Familiarity with cloud platforms such as AWS SageMaker, familiarity working with relational databases.

• Solid understanding of probability, statistics, and experimental design.

• Experience with object-oriented and functional programming, version control (Git), and software engineering best practices.

• Detail-oriented, organized, and able to manage multiple priorities effectively.

• Excellent communication skills, capable of explaining complex technical results to diverse audiences.