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Machine Learning Engineer Quantization Jobs in Blacksburg, VA

Python Tutor

Salem, VA · Remote

$18 - $40/hr

Emphasizes readable, maintainable code and connects Python to machine learning, web scraping, scientific computing, and DevOps applications. * Curriculum Awareness & Adaptive Instruction: Familiar ...

Python Tutor

Blacksburg, VA · Remote

$18 - $40/hr

Emphasizes readable, maintainable code and connects Python to machine learning, web scraping, scientific computing, and DevOps applications. * Curriculum Awareness & Adaptive Instruction: Familiar ...

Preferred Qualifications Knowledge of quantum error correction, tensor networks, and/or machine learning. Experience with programming and numerical methods is preferred. Overtime Status Exempt: Not ...

... machine learning and molecular modeling approaches to guide methodology development and process ... Research experiences in food science and engineering Self-motivated, be able to work independently ...

Preferred Qualifications Experience with the scientific computing library and parallel programming. Experience with writing scientific articles. Experience with writing scientific machine learning.

... data science, engineering, and advanced mathematics. * Conceptual Teaching & Problem-Solving ... machine learning, and quantum mechanics applications. * Curriculum Awareness & Adaptive Instruction:

Linear Algebra Tutor

Salem, VA · Remote

$18 - $40/hr

... data science, engineering, and advanced mathematics. * Conceptual Teaching & Problem-Solving ... machine learning, and quantum mechanics applications. * Curriculum Awareness & Adaptive Instruction:

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for ... transfer, machine design, manufacturing processes, and control systems. Ability to explain free ...

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Machine Learning Engineer Quantization information

See Blacksburg, VA salary details

$27.6K

$113K

$169.7K

How much do machine learning engineer quantization jobs pay per year?

As of Jul 16, 2026, the average yearly pay for machine learning engineer quantization in Blacksburg, VA is $112,958.00, according to ZipRecruiter salary data. Most workers in this role earn between $89,000.00 and $136,000.00 per year, depending on experience, location, and employer.

What are some common challenges Machine Learning Engineers face when implementing quantization techniques in production models?

Machine Learning Engineers working on quantization often encounter challenges such as balancing reduced model size and computational efficiency with maintaining acceptable accuracy levels. Adapting quantization methods to different hardware platforms can also require significant testing and optimization. Additionally, engineers must frequently address compatibility issues with existing deployment pipelines and ensure that quantization-aware training is properly integrated to minimize performance degradation. Collaboration with hardware and software teams is essential to streamline deployment and achieve optimal results.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer Quantization, and why are they important?

To thrive as a Machine Learning Engineer Quantization, you need a solid background in machine learning, deep learning, and computer science, typically supported by a degree in a related field. Familiarity with quantization techniques, frameworks such as TensorFlow Lite or PyTorch, and experience with hardware accelerators are crucial. Strong problem-solving skills, attention to detail, and effective collaboration set top performers apart. These capabilities are vital for efficiently deploying high-performing models on resource-constrained devices and ensuring scalable, real-world AI solutions.

What does a Machine Learning Engineer Quantization do?

A Machine Learning Engineer specializing in quantization focuses on optimizing machine learning models by reducing their size and computational requirements without significantly sacrificing accuracy. This involves converting model parameters and computations from high-precision formats (like 32-bit floating point) to lower-precision formats (such as 8-bit integers). Quantization enables faster inference, lower memory usage, and allows models to run efficiently on edge devices and mobile platforms. These engineers work closely with data scientists and hardware teams to implement, test, and validate quantized models in production environments.

What is the difference between Machine Learning Engineer Quantization vs Data Scientist?

AspectMachine Learning Engineer QuantizationData Scientist
Required CredentialsBachelor's or master's in CS, ML, or related; certifications in ML or AIBachelor's or master's in statistics, CS, or related; certifications in data analysis or statistics
Work EnvironmentDeveloping optimized ML models, deploying quantized models for efficiencyAnalyzing data, building predictive models, interpreting results
Industry UsageTech companies, AI hardware firms, embedded systemsFinance, healthcare, marketing, research institutions

Machine Learning Engineer Quantization focuses on optimizing ML models for deployment efficiency, often working closely with hardware and software teams. Data Scientists analyze data and build models for insights. While both roles require ML knowledge, quantization engineers specialize in model compression techniques, whereas data scientists focus on data analysis and interpretation.

What are popular job titles related to Machine Learning Engineer Quantization jobs in Blacksburg, VA? For Machine Learning Engineer Quantization jobs in Blacksburg, VA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Quantization jobs in Blacksburg, VA look for? The top searched job categories for Machine Learning Engineer Quantization jobs in Blacksburg, VA are:
What cities near Blacksburg, VA are hiring for Machine Learning Engineer Quantization jobs? Cities near Blacksburg, VA with the most Machine Learning Engineer Quantization job openings:
Postdoctoral Associate - Transportation Packaging and Distribution Systems Research

Postdoctoral Associate - Transportation Packaging and Distribution Systems Research

Virginia Polytechnic Institute and State University

Blacksburg, VA • On-site

Full-time

Re-posted 25 days ago


Virginia Tech rating

7.8

Company rating: 7.8 out of 10

Based on 65 frontline employees who took The Breakroom Quiz

203rd of 555 rated colleges and universities


Job description

Postdoctoral Associate - Transportation Packaging and Distribution Systems Research
Job no: 534892
Work type: Research Faculty
Senior management: Natural Resources
Department: Sustainable Biomaterials
Location: Blacksburg, Virginia
Categories: Research / Scientific, Natural Resources
Job Description
Transportation Packaging and Distribution Systems Research
The department for Sustainable Biomaterials and its Center for Packaging and Unit Load Design (CPULD) at Virginia Tech are an internationally recognized leader in distribution packaging research and innovation. Our state-of-the-art laboratory facilities include advanced testing equipment for vibration analysis, shock dynamics, compression testing, and thermal performance evaluation. Through collaborative partnerships with industry and government agencies, we conduct cutting-edge research in transport packaging performance, unit load design, cold chain logistics, and sustainable packaging systems, addressing critical challenges in product protection, supply chain optimization, and distribution system efficiency.
We seek a highly motivated and collaborative researcher with expertise in engineering, materials science, or related computational fields to contribute to our research program focused on transport packaging optimization and distribution systems. Current research initiatives include developing advanced vibration analysis methodologies for wooden packaging systems, predictive modeling for packaging performance, and optimization of packaging designs for high-value product distribution. The ideal candidate will bring strong analytical and experimental skills to address challenges in packaging dynamics, product protection, and sustainable packaging solutions.
The individual will be responsible for:
• Design and conduct experimental research on transport packaging performance, including vibration, shock, and compression testing of packaging systems.
• Develop and implement predictive models for packaging performance using machine learning approaches and physics-based simulations.
• Investigate logistics optimization strategies for packaging and distribution systems, including unit load design and supply chain efficiency.
• Collect, process, and analyze experimental data from mechanical testing equipment; prepare technical reports and presentations.
• Conduct comprehensive literature review and analysis of existing vibration data conversion methodologies, assessing their applicability to wooden packaging systems.
• Develop practical design guidelines, simplified analytical models, and best practices documentation for industry implementation of vibration testing and design optimization.
• Prepare research findings for publication in peer-reviewed journals and present results at national and international conferences.
• Collaborate with industry partners on applied research projects and technology transfer initiatives, including workshop delivery and distribution of design tools.
• Mentor graduate students on research projects and laboratory procedures.
• Participate in the development of grant proposals for external funding from NSF, USDA NIFA, and other federal agencies.
• Limited teaching or outreach responsibilities may be assigned depending on interest and departmental needs.
• Given the nature of this work, this position is not eligible for telework.
Required Qualifications
• Ph.D. in Packaging Science, Wood Science, Mechanical Engineering, Industrial and Systems Engineering, Wood Science, or related field. PhD must be awarded no more than four years prior to the effective date of appointment with a minimum of one year eligibility remaining.
• Strong background in computational mechanics, structural dynamics, or wood mechanics.
• Experience with finite element analysis and modal analysis techniques.
• Experience with vibration analysis, dynamic testing, or mechanical systems characterization.
• Proven record of publishing refereed journal articles and presenting at professional conferences.
• Strong understanding of experimental design, data collection, and statistical analysis.
• Ability to work both independently and collaboratively in an interdisciplinary research environment.
• Excellent technical writing, communication, and interpersonal skills.
• Ability to lift or carry 50 lbs, obtain and maintain a valid Virginia Driver's license.
• Position requires considerable walking, standing, stooping, and bending, and requires the use of personal protective equipment.
Preferred Qualifications
• Experience applying machine learning models to structural systems, predictive modeling, or optimization problems related to packaging or mechanical systems.
• Background in logistics optimization, supply chain modeling, or distribution network analysis.
• Familiarity with vibration data analysis techniques.
• Experience with Monte Carlo simulation, uncertainty quantification, or sensitivity analysis.
• Programming skills in Python, MATLAB, R, or similar computational platforms.
• Experience with data acquisition systems, accelerometer arrays, and sensor integration for dynamic testing.
• Knowledge of packaging performance testing standards (ASTM D4169, ISTA) and test protocol development.
• Experience with wood material properties, orthotropic material modeling, or joint behavior under cyclic loading.
Pay Band
{lPayScaleID}
Overtime Status
Exempt: Not eligible for overtime
Appointment Type
Restricted
Salary Information
Hours per week
40
Review Date
April 30, 2026
Additional Information
The successful candidate will be required to have a criminal conviction check.
About Virginia Tech
Dedicated to its motto, Ut Prosim (That I May Serve), Virginia Tech pushes the boundaries of knowledge by taking a hands-on, transdisciplinary approach to preparing scholars to be leaders and problem-solvers. A comprehensive land-grant institution that enhances the quality of life in Virginia and throughout the world, Virginia Tech is an inclusive community dedicated to knowledge, discovery, and creativity. The university offers more than 280 majors to a diverse enrollment of more than 36,000 undergraduate, graduate, and professional students in eight undergraduate colleges, a school of medicine, a veterinary medicine college, Graduate School, and Honors College. The university has a significant presence across Virginia, including Blacksburg, the greater Washington, D.C. area, the Health Sciences and Technology Campus in Roanoke, sites in Newport News and Richmond, and numerous Extension offices and research institutes. A leading global research institution, Virginia Tech conducts more than $650 million in research annually.
Virginia Tech endorses and encourages participation in professional development opportunities and university shared governance. These valuable contributions to university shared governance provide important representation and perspective, along with opportunities for unique and impactful professional development.
Virginia Tech does not discriminate against employees, students, or applicants on the basis of age, color, disability, sex (including pregnancy), gender, gender identity, gender expression, genetic information, ethnicity or national origin, political affiliation, race, religion, sexual orientation, or military status, or otherwise discriminate against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees or applicants, or on any other basis protected by law.
If you are an individual with a disability and desire an accommodation, please contact Trish Colley at colleyp@vt.edu during regular business hours at least 10 business days prior to the event.
Advertised: March 24, 2026
Applications close:
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About Virginia Tech

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Virginia Tech, guided by its motto "Ut Prosim" (That I May Serve), embraces a hands-on, interdisciplinary approach to educate scholars as leaders and problem-solvers. As a comprehensive land-grant institution, it enriches the quality of life in Virginia and worldwide, fostering an inclusive community focused on knowledge, discovery, and creativity. With over 280 majors, the university serves a diverse student body of more than 36,000 across undergraduate, graduate, and professional programs. Virginia Tech's presence extends throughout Virginia, including campuses in Northern Virginia, Roanoke, Newport News, and Richmond, along with multiple Extension offices and research centers. As a prominent global research institution, it conducts over $500 million in research annually.

Industry

Colleges, universities, and professional schools

Company size

5,001 - 10,000 Employees

Headquarters location

Blacksburg, VA, US

Year founded

1872

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