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Machine Learning Engineer Quantization Jobs in Colorado Springs, CO

Nou Systems Inc

DevOps Engineer

Colorado Springs, CO · On-site

$120K - $165K/yr

You will help deploy, scale, and standardize machine learning development workflows in support of ... s Engineer, you will help architect, implement, and maintain modern ML development pipelines. You ...

Agile Defense

Data Scientist

Colorado Springs, CO

This role is part of a multidisciplinary team integrating advanced analytics, machine learning, and engineering practices into mission-critical environments at Combatant Commands. You will help shape ...

Agile Defense

Data Engineer - NORTHCOM

Colorado Springs, CO · On-site

$150K - $170K/yr

This role is part of a multidisciplinary team integrating advanced analytics, machine learning, and engineering practices into mission-critical environments at Combatant Commands. You will help shape ...

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All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in Colorado Springs, CO

Machine Learning Engineer Quantization information

See Colorado Springs, CO salary details

$31K

$126.9K

$190.7K

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

As of Jul 30, 2026, the average yearly pay for machine learning engineer quantization in Colorado Springs, CO is $126,901.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,000.00 and $152,800.00 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 Colorado Springs, CO look for? The top searched job categories for Machine Learning Engineer Quantization jobs in Colorado Springs, CO are:
What cities near Colorado Springs, CO are hiring for Machine Learning Engineer Quantization jobs? Cities near Colorado Springs, CO 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 Colorado Springs, CO as of July 2026, with employment types broken down into 89% Full Time, 8% Part Time, and 3% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution, with an average salary of $126,901 per year, or $61 per hour.

Machine Learning Engineer

nou Systems, Inc.

Colorado Springs, CO • On-site

Full-time

Re-posted 16 days ago

Job description

Job Summary:
nou Systems, Inc. is a 100% ESOP company focused on solving challenging defense problems. They are seeking a Machine Learning Engineer to design, develop, and deploy machine learning capabilities for defense applications, collaborating with teams to transition ML concepts into practical solutions.
Responsibilities:
• Develop, train, evaluate, and analyze machine learning models using Python and PyTorch, writing modular, maintainable, and testable code.
• Build backend software components that support ML workflows, data processing, model evaluation, and system integration.
• Help identify, prepare, and validate training and evaluation datasets.
• Implement and adapt ML methods from open literature, including supervised learning and deep learning approaches.
• Work in Linux-based, containerized development environments using VS Code Dev Containers, Remote-SSH, Docker/Podman, and contribute to continuous improvement of development processes.
• Use GitLab-based workflows for source control, issue tracking, merge requests, code review, and collaboration.
• Communicate technical results clearly through written documentation, presentations, and team discussions.
• Support multiple project teams and help translate ML concepts into practical engineering solutions.
Qualifications:
Required:
• Bachelor’s degree in computer science, mathematics, software engineering, data science, or a closely related technical field.
• 3+ years of professional experience in machine learning, data science, or backend software development.
• Hands-on experience developing, training, or evaluating deep learning models using PyTorch.
• Professional experience building backend software, data pipelines, APIs, or system integration components.
• U.S. citizenship and the ability to obtain a Secret security clearance.
Preferred:
• Strong communication skills, intellectual curiosity, and comfort working across ML, software, and mission-domain teams. Our work often requires creative, multidisciplinary approaches.
• Understanding of core ML and statistical concepts (bias-variance tradeoff, data mismatch, sample sufficiency)
• Comfort with self-direction. You'll work with our top technical talent, but we're looking for evidence you can diagnose a problem and approach it strategically.
• Experience with Docker, Podman, or similar containerization tools, including editing Dockerfiles or container configuration.
• Experience with ML tools (MLflow, Optuna, or PyTorch Lightning)
• Experience with RL development using Gymnasium and Ray RLlib.
• Experience with LLMs, generative AI tools, vector databases, or frameworks such as LangChain or LlamaIndex.
• Familiarity with CI/CD pipelines, Kubernetes, DevSecOps practices, secure artifact repositories, or deployment in restricted DoD environments.
Company:
no Systems is a consultant offering engineering and technical services to the government in Huntsville, Alabama. Founded in 2011, the company is headquartered in Huntsville, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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