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Machine Learning Engineer Quantization Jobs in Ohio

Crowe LLP

Machine Learning Engineer

Columbus, OH · On-site

$62K - $100K/yr

As an AI Engineering team member, you will be instrumental in advancing new features and/or solutions from the Proof of Concept stage to full production readiness. Your role involves refining and ...

Crowe LLP

Machine Learning Engineer

Cleveland, OH

$62K - $100K/yr

As an AI Engineering team member, you will be instrumental in advancing new features and/or solutions from the Proof of Concept stage to full production readiness. Your role involves refining and ...

Workiva

$40/hr

Implement tooling and features to support machine learning model development and deployment under the direction of a full-time Machine Learning Engineer * Help integrate tools such as LlamaIndex and ...

Workiva

$40/hr

Implement tooling and features to support machine learning model development and deployment under the direction of a full-time Machine Learning Engineer * Help integrate tools such as LlamaIndex and ...

Workiva

$40/hr

Implement tooling and features to support machine learning model development and deployment under the direction of a full-time Machine Learning Engineer * Help integrate tools such as LlamaIndex and ...

Workiva

$40/hr

Implement tooling and features to support machine learning model development and deployment under the direction of a full-time Machine Learning Engineer * Help integrate tools such as LlamaIndex and ...

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Machine Learning Engineer Quantization information

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 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 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 Ohio?

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What job categories do people searching Machine Learning Engineer Quantization jobs in Ohio look for?

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What cities in Ohio are hiring for Machine Learning Engineer Quantization jobs?

Cities in Ohio with the most Machine Learning Engineer Quantization job openings:

Machine Learning Engineer Quantization jobs near you

Artificial Intelligence/Machine Learning Engineer (Pending Contract Award)

Design Interactive

Dayton, OH • On-site

$110 - $160/hr

Other

Re-posted 24 days ago

Job description

Position: Artificial Intelligence/Machine Learning Engineer (Pending Contract Award)

Location: Dayton, OH

Job Id: 146

# of Openings: 1

Pipeline Requisition – Proposal (Anticipated Start: September 2026)

Location: Wright Patterson Air Force Base, Dayton, OH (subject to award)

Contract Type: Proposal Pipeline – Positions contingent upon contract award

Overview

We are building a talent pipeline in support of an upcoming contract proposal to provide advisory and assistance services to Air Force Life Cycle Management Center (AFLCMC) to support the National Air and Space Intelligence Center (NASIC) mission areas including engineering, analysis, information technology and cyber security, with anticipated work beginning in September 2026. This position will support NASIC’s production of technical intelligence; modernize capabilities; support intelligence community collaboration and Department of War (DoW) mission needs.

Work will be performed on-site at Wright Patterson Air Force Base, Dayton, OH, pending award.

Required Qualifications
  • Ability to obtain and maintain a DoD security clearance (Secret or TS/SCI depending on role)
  • Strong background directly aligned with the technical or analytical specialty area
  • Strong analytical, communication, and writing skills
  • For some roles, advanced degrees (MS/PhD) are preferred
Desired Qualifications
  • Experience supporting DoW, interagency, or national security missions is highly desirable
  • Experience within the National Air and Space Intelligence Center (NASIC) community.

We are currently gathering interest and pre-qualifying candidates for the potential role described below:

Artificial Intelligence/Machine Learning Engineer

The Artificial Intelligence/Machine Learning (AI/ML) Engineer provides engineering support to deliver secure, low‑risk technical solutions and effective program execution, including the integration of intelligent automation, AI, and machine learning. Responsibilities include developing and managing programs to guide digital transformation to cloud computing environments and the implementation of intelligent automation, AI and ML capabilities. The engineer leads programs that guide the application of hardware, software, and policy to modernize and adapt legacy systems for hybrid classified cloud environments. Additionally, the engineer develops and oversees programs that enable the adoption and operationalization of IA/AI/ML solutions, ensuring alignment with mission priorities, data strategies, and security requirements.

Desired Experience
  • Bachelor’s degree in computer science/engineering or related discipline
  • Minimum 5 years of related experience.
  • Requires proficiency in complete software development lifecycle.
  • 3+ years of experience in AL/ML-based solutions, including Python programming and deep learning frameworks such as PyTorch, TensorFlow, or Keras
  • Experience developing Natural Language Processing (NLP), Large Language Models (LLM), and Retrieval-Augmented Generation (RAG)-enabled solutions
  • Experience applying AI/ML research to develop AI/ML solutions
  • Knowledge of core AI/ML concepts, including clustering, regression, classification, algorithm selection, and model evaluation
  • Requires good oral and written communication skills, interpersonal skills, including ability to adapt and work effectively in a variety of configurations independently, as a member of a team, and in a matrix reporting environment.
  • Requires ability to prioritize tasks, show considerable initiative, resolve conflicts and handle complaints.

We encourage applications from candidates of all backgrounds, experiences, and abilities—including those from underrepresented communities in technology.

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