AI Engineer (AI+CryoET)

Location: Janelia Research Campus, 19700 Helix Drive, Ashburn, Virginia, United States

Hiring pay range: $100,178.62–$239,789.68 annual salary, depending on level and qualifications.

Company

hirify.global supports AI-driven scientific research and interdisciplinary work in biology, imaging, molecular dynamics, and machine learning.

What you will do

• Develop and evaluate deep learning models for detecting and localizing gold nanoparticles, nucleosomes, synaptic receptors, and other macromolecular particles in cryoET data.
• Build methods that use gold nanoparticle detections to improve tomogram reconstruction, tilt-series alignment, and deformation correction under low signal-to-noise conditions.
• Design rigorous training and evaluation pipelines that address missing-wedge artifacts, CTF effects, and sim-to-real transfer from molecular-dynamics-derived synthetic data.
• Identify where human annotation and proofreading are needed, and guide annotation efforts.
• Maintain reproducible, well-documented code; contribute to publications and conference presentations.
• Collaborate with cryoET experts, structural biologists, computer scientists, and researchers across multiple institutions.

Requirements

• Master’s or PhD in Computer Science, Applied Mathematics, Physics, Computational Chemistry, or a related field, or equivalent experience.
• At least 3 years of experience training and evaluating deep learning models, particularly with 3D or volumetric image data.
• Strong Python skills and proficiency with PyTorch and/or JAX.
• Experience with rigorous experimental design, model comparisons, ablation studies, and reproducibility.
• Experience with scalable GPU computing environments on Linux HPC clusters and high-throughput processing of large-scale data.
• Strong communication skills, commitment to open science, and interest in interdisciplinary collaboration.

Nice to have

• Experience with cryo-EM/ET processing, tomographic reconstruction, or imaging inverse problems.
• Familiarity with molecular dynamics simulations or synthetic data generation, including OpenMM or LAMMPS.
• Experience with differentiable rendering, neural radiance fields, or analysis-by-synthesis approaches for 3D reconstruction.
• Knowledge of IMOD, Warp, RELION, AreTomo, MRC, or Zarr.
• Experience with template matching, sub-tomogram averaging, or particle picking in cryo-EM/ET.

Culture & Benefits

• Comprehensive health and welfare benefits, retirement plans, paid time off, and wellness programs.
• Access to GPU-based computational infrastructure and high-quality cryoET datasets.
• Supportive, collaborative environment with access to structural biology, cryoET, molecular dynamics, software engineering, and AI/ML expertise.
• On-site childcare, free gyms, on-campus housing, social and dining spaces, and shuttle service from the Washington, D.C. metro area.
• Opportunity to partner with frontier AI labs on scientific applications of AI.

Hiring process

• Submit a cover letter describing an executed deep learning project, including results, limitations, challenges, and individual contributions.
• Include links to relevant code repositories and a GitHub, GitLab, personal website, or similar portfolio.