AI Research Specialist in Computational Biology - TERM

Core Responsibilities

Lead complex biological data analysis projects from framing through delivery, applying data science and AI techniques to extract meaningful patterns, trends, and variables from large and heterogeneous datasets.

Design and build interactive dashboards that allow scientists to visualize, explore, manipulate, and curate biological data.

Build and maintain the full-stack platforms and backend infrastructure that support those dashboards.

Develop APIs and data pipelines that expose laboratory data and analysis results to both human collaborators and autonomous AI systems.

Apply embedding-based approaches — across natural language, biological sequence, and other modalities — to problems where they offer analytical leverage.

Work directly with experimental scientists to understand their goals, elicit requirements, and translate them into working tools and analyses.

Communicate complex results to technical and non-technical audiences in a form they can grasp quickly.

Position Requirements

• RD!: Minimum of a Bachelor's degree in computational biology, bioinformatics, data science, computer science, or a closely related field.
• Biology domain expertise: A background in biology sufficient to reason independently about biological data and to engage substantively with experimental scientists.
• Python: Demonstrated proficiency as a primary working language for data science and application development.
• Data science: Experience analyzing complex, heterogeneous datasets to identify important trends, relationships, and variables of interest.
• Visual dashboard development: Demonstrated experience building dashboards for data analysis, visualization, and curation by human users.
• Backend and API development: Experience building the backend services and APIs that support data-facing applications.
• Data embeddings: A working understanding of how embedding technology functions and the ability to apply it creatively across domains — including language embeddings and protein sequence embeddings.
• Co-working with AI: Practical experience using large language models as a primary tool for building software and conducting analysis.
• Collaboration and communication: The ability to present complex results clearly and quickly to varied audiences, and to work with scientists to elicit and understand their underlying needs.

Preferred

• Experience with multimodal data analysis on biological datasets, working across data modalities (e.g., imaging or video alongside structured or sequence data).
• Experience with laboratory automation, instrument data streams, or agent-driven experimental systems.
• Prior work supporting research groups or scientific collaborators in a service or partnership capacity.
• Familiarity with genome annotation, metabolic modeling, or systems biology (e.g., KBase, ModelSEED, KEGG).