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Machine Learning Engineer Quantization Jobs in White Plains, MD

10a Labs

Machine Learning Engineer

Washington, DC · On-site +1

$130K - $200K/yr

We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build, evaluate, and deploy advanced machine learning systems across a range of safety, security, and ...

10a Labs

Machine Learning Engineer

Washington, DC · On-site

$130K - $200K/yr

We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build, evaluate, and deploy advanced machine learning systems across a range of safety, security, and ...

Kitware

Machine Learning Engineer

Arlington, VA · Hybrid

$110K - $160K/yr

... machine learning, artificial intelligence, and computer vision * Perform rapid prototyping and enhanced development to be integrated into operational systems * Contribute your strong programming ...

Kitware

Machine Learning Engineer

Arlington, VA · Hybrid

$110K - $160K/yr

... machine learning, artificial intelligence, and computer vision * Perform rapid prototyping and enhanced development to be integrated into operational systems * Contribute your strong programming ...

Kitware

Machine Learning Engineer

Arlington, VA · On-site

$110K - $160K/yr

... machine learning, artificial intelligence, and computer vision * Perform rapid prototyping and enhanced development to be integrated into operational systems * Contribute your strong programming ...

Hirekeyz Inc

Sr. Machine Learning Engineer

Fort Belvoir, VA · On-site

$118K - $162K/yr

Role: Sr. Machine Learning Engineer Location: Ft. Belvoir, VA (On-site with Hybrid Option) Duration: Long Term Contract Clearance: DOD Top Secret Clearance (Must) As a consultant, will be working to ...

STR

Machine Learning Engineer

Arlington, VA · On-site

$115K - $140K/yr

The Role As a Machine Learning Engineer, you will help develop and integrate cutting-edge AI/ML models into production systems that solve critical national security problems. Working as part of a ...

STR

Machine Learning Engineer

Arlington, VA

$115K - $140K/yr

The Role As a Machine Learning Engineer, you will help develop and integrate cutting-edge AI/ML models into production systems that solve critical national security problems. Working as part of a ...

STR

Machine Learning Engineer

Arlington, VA · On-site

$115K - $140K/yr

The Role As a Machine Learning Engineer, you will help develop and integrate cutting-edge AI/ML models into production systems that solve critical national security problems. Working as part of a ...

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Showing results 41-60

All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in White Plains, MD

Machine Learning Engineer Quantization information

See White Plains, MD salary details

$30.4K

$124.3K

$186.8K

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

As of Aug 17, 2026, the average yearly pay for machine learning engineer quantization in White Plains, MD is $124,302.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,000.00 and $149,600.00 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 job categories do people searching Machine Learning Engineer Quantization jobs in White Plains, MD look for?

The top searched job categories for Machine Learning Engineer Quantization jobs in White Plains, MD are:

What cities near White Plains, MD are hiring for Machine Learning Engineer Quantization jobs?

Cities near White Plains, MD with the most Machine Learning Engineer Quantization job openings:

Machine Learning Engineer Quantization jobs near you
Infographic showing various Machine Learning Engineer Quantization job openings in White Plains, MD as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 21% Part Time, 1% Temporary, and 3% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $124,302 per year, or $59.8 per hour.

Machine Learning Engineer Role

OpenDataJobs

Washington, DC

Full-time

Posted 9 days ago

Job description

The work

Machine Learning Engineers make machine learning and AI models reproducible, deployable, scalable, and supportable. They build the path from training data and experimentation to a versioned model service that can be released, monitored, retrained, and retired without guesswork.

The role centers on the model lifecycle and the platform beneath it. Machine Learning Engineers automate training and validation, manage features and model artifacts, optimize inference, implement machine learning operations (MLOps), and watch for changes in data, behavior, performance, reliability, and cost. They create the shared tooling that lets data scientists and application engineers move models into production safely.

What you'll build

        Reproducible training, validation, tuning, and retraining pipelines with versioned data, code, parameters, environments, and model artifacts.

        Model-serving systems and APIs designed for appropriate latency, throughput, availability, scaling, and rollback.

        Feature pipelines, feature stores, model registries, lineage records, approval workflows, and automated release controls.

        Monitoring and alerting for data quality, drift, model performance, fairness, infrastructure health, latency, and cost.

        Reusable libraries, templates, environments, and delivery pipelines that give data scientists a tested path from experiment to production.

Who you are

You are comfortable at the seam between modeling and software engineering. You can inspect a model, harden a pipeline, diagnose a production failure, and improve the platform so the same class of problem is easier to prevent next time.

You value repeatability over heroics. You work closely with data scientists on model behavior, data engineers on reliable inputs, AI Engineers on application integration, and platform and security teams on the environment in which the model runs.

What you bring

        Strong programming and software-engineering practice, including testing, version control, packaging, automation, and production debugging.

        Working knowledge of model development, evaluation metrics, feature engineering, data splitting, tuning, and the limits of different modeling approaches.

        Experience with training and inference pipelines, containers, cloud or on-premises compute, artifact management, and automated deployment.

        Practical MLOps experience with model registries, lineage, reproducibility, monitoring, drift analysis, retraining, release controls, and rollback.

        The ability to balance model quality with reliability, interpretability, security, privacy, latency, throughput, and cost.

About OPEN Data Jobs

OPEN Data Jobs connects AI, data, and software professionals with critical roles, primarily in the federal sector. Registering with ODJ can put your profile in view for multiple positions across several clients.

Register for Machine Learning Engineer Role

Click Apply below to register for Machine Learning Engineer Role.

Requirements

What openings may require

An opening may emphasize predictive models, computer vision, natural language models, ranking, anomaly detection, recommender systems, edge inference, generative AI model operations, or an enterprise ML platform. Some openings will focus more on model development, while others will focus more on serving and platform engineering.

Specific openings may name Python, SQL, Java, model frameworks, distributed-processing tools, cloud ML services, container orchestration, graphics processing units, feature stores, model registries, experiment tracking, or infrastructure as code. OPEN Data Jobs will identify the required depth for each opening

Benefits

Compensation, benefits, work location, and employment terms are set for each specific opening and will be stated with that opening

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