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Nanotechnology Engineering Jobs in Reston, VA (NOW HIRING)

J5 Consulting

Network Engineer

Herndon, VA · On-site

The Sponsor requires network engineering support with a deep subject matter expertise with ... Micro, Nano, TL-10G, TL-1g, and next generation crypto devices, including their configuration ...

J5 Consulting

Network Engineer

Herndon, VA · On-site

The Sponsor requires network engineering support with a deep subject matter expertise with ... Micro, Nano, TL-10G, TL-1g, and next generation crypto devices, including their configuration ...

GAP SOLUTIONS INC

Biologist (50849)

Gaithersburg, MD · On-site

Support mRNA-Lipid Nanoparticle (LNP) program by designing and executing process development ... Bachelor's degree in Bioengineering, Chemical Engineering, Biochemistry, or a related discipline ...

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Showing results 41-60

All JobsNanotechnology Engineering JobsNanotechnology Engineering Jobs in Reston, VA

Nanotechnology Engineering information

See Reston, VA salary details

$41.6K

$75.8K

$107.2K

How much do nanotechnology engineering jobs pay per year?

As of Sep 5, 2026, the average yearly pay for nanotechnology engineering in Reston, VA is $75,782.00, according to ZipRecruiter salary data. Most workers in this role earn between $63,500.00 and $86,300.00 per year, depending on experience, location, and employer.

What is nanotechnology engineering?

Nanotechnology engineering is a field that involves the design, development, and application of materials and devices on the scale of nanometers—one billionth of a meter. It combines principles from physics, chemistry, biology, and engineering to manipulate matter at the atomic and molecular level. Nanotechnology engineers work on creating new materials, improving existing products, and developing innovative solutions in areas such as medicine, electronics, energy, and the environment. The field is highly interdisciplinary and rapidly evolving, offering diverse career opportunities.

What are the key skills and qualifications needed to thrive as a nanotechnology engineer?

To thrive as a Nanotechnology Engineer, you need a strong background in physics, chemistry, materials science, and engineering, usually supported by at least a bachelor's degree in a related field. Proficiency with tools such as electron microscopes, atomic force microscopes, and simulation software, as well as knowledge of cleanroom protocols and possibly certifications in nanofabrication, is essential. Strong analytical thinking, attention to detail, and effective teamwork and communication skills help professionals excel in research and cross-disciplinary projects. These skills and qualifications are crucial for developing innovative solutions, maintaining safety, and advancing applications in sectors like medicine, electronics, and energy.

What are some common challenges faced by nanotechnology engineers when working on interdisciplinary teams?

Nanotechnology engineers frequently collaborate with professionals in fields such as chemistry, biology, materials science, and physics. A common challenge is communicating complex technical concepts across disciplines with differing terminologies and priorities. Successfully bridging these gaps requires strong teamwork, adaptability, and an openness to learning from others. Building these skills not only enhances project outcomes but also creates more opportunities for career advancement in this rapidly evolving field.

What is the difference between Nanotechnology Engineering vs Materials Science Engineering?

AspectNanotechnology EngineeringMaterials Science Engineering
Required CredentialsBachelor's or higher in Nanotechnology or related fieldsBachelor's or higher in Materials Science or Engineering
Work EnvironmentResearch labs, manufacturing, R&D departmentsManufacturing plants, research labs, product development
Industry UsageElectronics, medicine, energy, materialsAutomotive, aerospace, consumer goods, manufacturing
Common Search/ComparisonYesYes

Nanotechnology Engineering and Materials Science Engineering share overlapping skills and work environments, often collaborating in research and development. However, Nanotechnology Engineering focuses specifically on manipulating matter at the atomic and molecular levels, while Materials Science Engineering covers a broader range of materials and their properties. Both fields are vital in advancing technology and innovation, with Nanotechnology Engineering offering specialized expertise in nanoscale applications.

Is nanotechnology engineering a good career?

Nanotechnology engineering is a growing field that involves designing and manipulating materials at the atomic and molecular levels, often requiring knowledge of physics, chemistry, and materials science. It offers opportunities in industries such as electronics, healthcare, and energy, with a demand for specialized skills and advanced degrees. Career prospects are favorable for those with strong technical backgrounds and experience with tools like microscopy and nanofabrication techniques.

What does a nanotechnology engineer do?

A nanotechnology engineer designs, develops, and tests materials and devices at the nanoscale, typically involving manipulation of structures less than 100 nanometers. They work with tools like electron microscopes and focus on applications in electronics, medicine, and materials science, often requiring knowledge of physics, chemistry, and engineering principles.

What are popular job titles related to Nanotechnology Engineering jobs in Reston, VA?

For Nanotechnology Engineering jobs in Reston, VA, the most frequently searched job titles are:

What job categories do people searching Nanotechnology Engineering jobs in Reston, VA look for?

The top searched job categories for Nanotechnology Engineering jobs in Reston, VA are:

What cities near Reston, VA are hiring for Nanotechnology Engineering jobs?

Cities near Reston, VA with the most Nanotechnology Engineering job openings:

Nanotechnology Engineering jobs near you
Infographic showing various Nanotechnology Engineering job openings in Reston, VA as of August 2026, with employment types broken down into 89% Full Time, 6% Part Time, 4% Contract, and 1% Nights. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution, with an average salary of $75,782 per year, or $36.4 per hour.

AI Engineer - AI+CryoET

Howard Hughes Medical Institute (HHMI)

Ashburn, VA • On-site

Full-time

Medical, Retirement

Re-posted 21 days ago

Key responsibilities

  • Develop and evaluate deep learning models for detecting and localizing particles and macromolecular structures in cryoET data.

  • Develop methods to leverage particle detections to improve tomogram reconstruction and address related challenges.

  • Design and execute AI model training and evaluation pipelines, including handling artifacts and transfer learning from synthetic data.

Job description

Primary Work Address: 19700 Helix Drive, Ashburn, VA, 20147
Current HHMI Employees, click here to apply via your Workday account.
TLDR: Build AI methods for 3D particle detection and structural analysis in cryo-electron tomography data, applied to chromatin organization and synaptic molecular targets.
Please include a cover letter with your application. Describe a deep learning project you have executed, ideally involving 3D image analysis, inverse problems, or physics-informed modeling. Cryo-EM/ET and computational structural biology projects are especially relevant. Discuss results, limitations, and challenges encountered. If the project was collaborative, describe your specific contributions. Include links to relevant code repositories and your GitHub/Gitlab profile, personal website, or similar evidence.
About the role:
AI@HHMI: HHMI is investing $500 million over the next 10 years to support AI-driven projects and to embed AI systems throughout every stage of the scientific process in labs across HHMI. This role is part of the AI+CryoET project within AI@HHMI, a multi-institutional project at the intersection of cryo-electron tomography (cryoET), molecular dynamics simulation, and machine learning. The project aims to develop AI methods for mesoscale structural biology, understanding how cellular macromolecules organize into higher-order structures. You will work in a team at Janelia, with experimental and computational collaborators across the Rosen lab (UT Southwestern Medical Center/HHMI), Gouaux lab (Oregon Health and Science University/HHMI), Collepardo-Guevara lab (University of Cambridge), and Villa lab (UC San Diego/HHMI).
You will develop machine learning methods for particle detection, localization, and structural analysis in cryoET data, with two interconnected aims: (1) detecting gold nanoparticle (AuNP) probes to improve reconstruction quality and identify molecular targets; (2) identifying the arrangement and connectivity of nucleosomes in chromatin that give rise to chromosome structure in cell nuclei and biochemical reconstitutions. This involves developing supervised and self-supervised AI models based on simulated as well as annotated experimental cryoET data, informed by molecular dynamics simulations of relevant biological structures. Success in this role requires close collaboration with cryoET experts, structural biologists, and computer scientists to ensure models work in challenging real-world scenarios of a biologically not yet fully understood system.
What we provide:
  • A competitive compensation package with comprehensive health and welfare benefits.
  • A supportive team environment that promotes collaboration and knowledge sharing.
  • Access to world-class computational infrastructure, GPU-based computing environments, and unique high-quality cryoET datasets.
  • The opportunity to work directly with leading structural biologists, cryoET experimentalists, and molecular dynamics experts on a highly interdisciplinary project.
  • The opportunity to engage with world-class researchers, software engineers, and AI/ML experts, contribute to impactful science, and be part of a dynamic community committed to advancing humanity's understanding of fundamental scientific questions.
  • Amenities that enhance work-life balance, such as on-site childcare, free gyms, available on-campus housing, social and dining spaces, and convenient shuttle bus service to Janelia from the Washington, D.C. metro area.
  • Opportunity to partner with frontier AI labs on scientific applications of AI. See https://www.anthropic.com/news/anthropic-partners-with-allen-institute-and-howard-hughes-medical-institute

What you'll do:
  • Develop and evaluate deep learning models for detecting and localizing gold nanoparticles and macromolecular particles (e.g., nucleosomes, synaptic receptors) in cryoET data, and for identification of nucleosome arrangement and connectivity in chromatin.
  • Develop methods to leverage gold nanoparticle detections to improve tomogram reconstruction, addressing challenges in tilt-series alignment, deformations, and low signal-to-noise conditions.
  • Design and execute rigorous AI model training and evaluation pipelines, including proper handling of missing wedge artifacts, CTF effects, and sim-to-real transfer from MD-derived synthetic training data.
  • Identify where additional human annotation and proofreading will be most helpful and design and guide annotation efforts.
  • Contribute to scientific publications, present findings at conferences, and maintain a well-documented codebase enabling seamless reproduction and extension of results.
  • Collaborate with interdisciplinary teams across multiple institutions.

What you bring:
  • Master's or PhD in Computer Science, Applied Mathematics, Physics, Computational Chemistry, or a related field, or equivalent combination of education and experience.
  • 3+ years training and evaluating deep learning models, particularly 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: 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 keen interest in working in a truly interdisciplinary environment.

Ways to stand out:
  • Experience with cryo-EM/ET data processing, tomographic reconstruction, or related inverse problems in imaging.
  • Familiarity with molecular dynamics simulations (e.g., OpenMM, LAMMPS) and/or synthetic data generation for training ML models.
  • Experience with differentiable rendering, neural radiance fields, or analysis-by-synthesis approaches for 3D reconstruction.
  • Knowledge of cryoET software tools (IMOD, Warp, RELION, AreTomo etc.) or microscopy data formats (MRC, Zarr).
  • Experience with template matching, sub-tomogram averaging, or particle picking in cryo-EM/ET contexts.

Physical Requirements:
Remaining in a normal seated or standing position for extended periods of time; reaching and grasping by extending hand(s) or arm(s); dexterity to manipulate objects with fingers, for example using a keyboard; communication skills using the spoken word; ability to see and hear within normal parameters; ability to move about workspace. The position requires mobility, including the ability to move materials weighing up to several pounds (such as a laptop computer or tablet).
Persons with disabilities may be able to perform the essential duties of this position with reasonable accommodation. Requests for reasonable accommodation will be evaluated on an individual basis.
Please Note:
This job description sets forth the job's principal duties, responsibilities, and requirements; it should not be construed as an exhaustive statement, however. Unless they begin with the word "may," the Essential Duties and Responsibilities described above are "essential functions" of the job, as defined by the Americans with Disabilities Act.
Hiring Pay Range
AI Engineer I: $100,178.62 - $125,223.28
AI Engineer II: $128,050.62 - $160,063.28
AI Engineer III: $155,495.81 - $194,369.76
AI Engineer IV: $191,831.74 - $239,789.68
Pay Type: Salary
The posted range reflects HHMI's good faith estimate of the anticipated hiring salary range for this role at the time of posting. Actual hiring compensation is determined by a candidate's qualifications, experience, and internal equity.
#LI-BG1
Compensation and Benefits
Our employees are compensated from a total rewards perspective in many ways for their contributions to our mission, including competitive pay, exceptional health benefits, retirement plans, time off, and a range of recognition and wellness programs. Visit our Benefits at HHMI site to learn more.
HHMI is an Equal Opportunity Employer
We use E-Verify to confirm the identity and employment eligibility of all new hires.

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