AI Engineer - AI+CryoET

About the Role

The position is part of the AI+CryoET project at HHMI, focused on developing AI methods for particle detection and structural analysis in cryo-electron tomography (cryo

ET) data. The role involves collaborating with experimental and computational scientists at several institutions to create supervised and self-supervised model architectures that can detect gold‑nanoparticle probes, identify nucleosome arrangements, and improve tomogram reconstructions.

• Develop and evaluate deep‑learning models for detecting and localizing gold nanoparticles and macromolecular particles (e.g., nucleosomes, synaptic receptors) in cryo

ET data.
• Design methods that use gold‑nanoparticle detections to improve tomogram reconstruction, addressing challenges such as tilt‑series alignment, deformations, and low signal‑to‑noise conditions.
• Build rigorous AI training and evaluation pipelines, including handling of missing‑wedge artifacts, CTF effects, and sim‑to‑real transfer from molecular‑dynamics‑derived synthetic training data.
• Identify where additional human annotation and proofreading will be most helpful and guide annotation efforts.
• Contribute to scientific publications, present findings at conferences, and maintain a well‑documented codebase that enables reproducibility and extension of results.
• Collaborate with interdisciplinary teams across multiple institutions.

• Master’s or PhD in Computer Science, Applied Mathematics, Physics, Computational Chemistry, or a related field, or an equivalent combination of education and experience.
• 3+ years training and evaluating deep‑learning models, especially on 3D or volumetric image data.
• Experience with detection, segmentation, or inverse problems in imaging is strongly preferred.
• Strong Python skills and proficiency in PyTorch and/or JAX.
• Ability to reason about neural‑network behavior from first principles: how architectural choices, regularization, and training procedures affect model behavior.
• Rigorous experimental design skills (model comparisons, ablation studies, reproducibility).
• Commitment to open science.
• Experience with scalable GPU‑based computing environments on Linux HPC clusters and high‑throughput processing for large‑scale data.
• Excellent communication skills and interest in interdisciplinary collaboration.
• Optional: experience with cryo‑EM/ET data processing, tomographic reconstruction, or related inverse problems; familiarity with molecular‑dynamics simulations (OpenMM, LAMMPS); knowledge of cryo
  
  ET software tools (IMOD, Warp, RELION, Are Tomo) or file formats (MRC, Zarr); experience with template matching or sub‑tomogram averaging; familiarity with differentiable rendering or neural radiance fields.

• Competitive compensation package with comprehensive health and welfare benefits.
• Supportive team environment that promotes collaboration and knowledge sharing.
• Access to world‑class computational infrastructure, GPU‑based computing environments, and unique high‑quality cryo
  
  ET datasets.
• Opportunities to work directly with leading structural biologists, cryo
  
  ET experimentalists, and molecular‑dynamics experts on highly interdisciplinary projects.
• Work‑life balance amenities such as on‑site childcare, free gyms, on‑campus housing, social and dining spaces, and a shuttle bus service to Janelia from the Washington, D.C. metro area.
• Partnership with frontier AI labs on scientific applications of AI.

HHMI is an Equal Opportunity Employer. We employ a rigorous process to evaluate and provide reasonable accommodations for all applicants.