AI Research Engineer, Multiomics and Imaging

Vanderbilt University•Nashville, TN

6d•Onsite

About The Position

The Biological Information Architecture (BIA) Lab at Vanderbilt University seeks a Research Engineer to build computational infrastructure, software, AI workflows, and methods for discovering how cells organize into tissues. The role offers substantial ownership of the lab’s technical direction, including software architecture, agentic AI systems for scientific analysis, computational methods, and robust tools for researchers. The lab works with atlas-scale multimodal data, combining advanced microscopy, quantitative cell phenomics, spatial tissue profiling, and multi-omics data, and collaborates across computational biology, imaging science, and other disciplines. The engineer will develop and integrate scalable workflows for multi-omics and imaging data, evaluate emerging methods and tools, establish engineering practices, and help train and onboard lab members. The position also contributes to publications, grants, community engagement, and assessment of technologies for open-source release or technology transfer, licensing, or commercialization. The position is a staff term appointment for a defined period, generally expected to exceed six months but not extend beyond one year; the incumbent will be evaluated after a 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.
Bachelor’s degree in a relevant scientific or computational field with strong computational emphasis; exceptional candidates with unconventional backgrounds are encouraged to apply.
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.
A minimum of 1–2 years of postgraduate or industry experience in software engineering, software development, or a related field is required, with exceptional undergraduate and master’s candidates 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 and HPC environments.
Excellent problem-solving skills and a passion for tackling difficult scientific challenges.
Ability to work independently, take ownership of technical direction, and operate with an incompletely specified roadmap.
Strong communication skills and ability to work effectively with technical and non-technical colleagues in interdisciplinary settings, including scientific writing.
Experience with version control and CI/CD pipelines.
Background in quantitative sciences such as computer science, engineering, physics, or statistics.
Evidence of mentorship or leadership in technical projects.
Self-motivated, enterprising, and intellectually curious.

Nice To Haves

Experience with multi-omics data types such as genomics, transcriptomics, or proteomics, and/or bioimage analysis such as 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.
Master’s or PhD degree; preferred but not required.
Experience with agentic and LLM frameworks such as Claude Agent SDK or LangChain.

Responsibilities

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