AI Research Engineer, Multiomics and Imaging

Vanderbilt University•Nashville, TN

About The Position

The Biological Information Architecture (BIA) Lab at Vanderbilt University seeks a self-motivated Research Engineer to build computational infrastructure, software, AI workflows, and methods for studying how cells organize into tissues. The role involves substantial ownership of the lab’s technical direction, including architecting software, developing agentic AI systems for scientific analysis, creating computational methods, and turning research ideas into robust tools used by researchers. The engineer will contribute to publications and grants, establish engineering practices, mentor trainees, and help identify technologies with potential for open-source release, licensing, technology transfer, or commercialization. The BIA Lab is an interdisciplinary computational biology group in Vanderbilt’s Department of Cell and Developmental Biology and is affiliated with the Center for Computational Systems Biology. Its work combines microscopy, quantitative cell phenomics, spatial tissue profiling, and multi-omics data. The lab develops open-source tools for bioimage analysis and computational biology and works with internal and external collaborators. This is a term staff position, generally expected to last more than six months but no longer than one year. The incumbent will be evaluated at the end of the one-year assignment for renewal.

Requirements

Strong software development and technical ability demonstrated through research, open-source work, internships, industry experience, competitions, or substantial independent projects.
Strong Python skills and familiarity with scientific libraries such as NumPy, SciPy, pandas, scikit-image, or scikit-learn.
Experience developing, releasing, and maintaining software projects.
Experience with, or strong interest in developing, agentic and LLM-based workflows.
Strong communication skills and ability to work effectively with technical and non-technical colleagues.
Self-motivation, initiative, intellectual curiosity, and comfort working with an incompletely specified roadmap.
A bachelor’s degree in a relevant scientific or computational field with strong computational emphasis, or equivalent demonstrated technical ability; exceptional candidates with unconventional backgrounds are encouraged to apply.
A minimum of 1–2 years of postgraduate or industry experience in software engineering, software development, or a related field is stated as required, while exceptional undergraduate and master’s candidates with substantial software-building experience are also encouraged to apply.
Strong software engineering experience, such as Python, C++, Rust, or Java, with a track record of building production systems.
Demonstrated ability to develop and deploy AI/ML models in research settings.
Proficiency in cloud platforms such as AWS, GCP, or Azure and in HPC environments.
Excellent problem-solving skills and ability to work independently and take ownership of technical direction.
Strong communication skills for interdisciplinary collaboration and scientific writing.
Experience with version control such as Git and CI/CD pipelines.
Background in quantitative sciences such as computer science, engineering, physics, or statistics.

Nice To Haves

Master’s or PhD degree.
Experience with multi-omics data, including genomics, transcriptomics, or proteomics, and/or bioimage analysis, including light-sheet, high-content, or spatial imaging.
Knowledge of workflow orchestration and parallel or distributed computing technologies such as Snakemake, Nextflow, NVIDIA CUDA, OpenMPI, or cloud-based HPC.
Experience with production-scale software engineering, scientific computing, or large-scale data systems.
Experience with multi-omics, imaging, or other high-dimensional biological data.
Evidence of mentorship or leadership in technical projects.

Responsibilities

Shape the lab’s technical vision and roadmap, including software architecture, development standards, and infrastructure choices.
Advise the PI on build-versus-adopt decisions and long-term technical strategy.
Design and build agentic, LLM-based workflows for multi-omics and imaging data ingestion, quality control, processing, and analysis.
Integrate agentic workflows with existing lab software and HPC or cloud computing infrastructure.
Architect and develop end-to-end software platforms and scalable computational pipelines for multi-omics, high-throughput cellular, and tissue data.
Design and implement agentic AI systems that automate scientific hypothesis generation and testing.
Research and assess emerging methodologies, algorithms, and open-source packages relevant to the lab’s research.
Turn promising research ideas into reliable, reusable, production-grade software, including documentation and automated tests.
Identify technologies with translational or commercial potential and support early-stage technology-transfer, licensing, or spin-out discussions.
Collaborate with biologists to translate experimental needs into technical solutions.
Contribute to scientific manuscripts, grant applications, and conference presentations.
Onboard and train graduate students, postdocs, and rotation students on lab software and computational workflows.
Promote and communicate lab software through open-source releases, conferences, GitHub, forums, workshops, and other community channels.
Establish and enforce engineering best practices within the lab.
Maintain and optimize cloud and HPC resources for large-scale data processing.