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Machine Learning Engineer Opt 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 ...

Agile Defense

Data Engineer - SPACECOM

Colorado Springs, CO · On-site

$145K - $165K/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 Opt JobsMachine Learning Engineer Opt Jobs in Colorado Springs, CO

Machine Learning Engineer Opt information

See Colorado Springs, CO salary details

$31K

$126.9K

$190.7K

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

As of Jul 30, 2026, the average yearly pay for machine learning engineer opt 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 Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models into production environments. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, reliable systems that organizations can use to make predictions or automate tasks. Their responsibilities include data preprocessing, choosing appropriate algorithms, model training, and ensuring the model's performance in real-world applications. Machine Learning Engineers often collaborate with data scientists, data engineers, and product teams to deliver intelligent solutions.

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

AspectMachine Learning Engineer OptData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; certifications in ML toolsBachelor's or Master's in CS, Statistics, or related fields; data analysis certifications
Work EnvironmentDevelops, tests, and deploys ML models in production systemsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, AI startups, e-commerce, financeResearch institutions, tech firms, consulting, finance
Common Search & ComparisonOften compared for technical skills and deployment focusCompared for data analysis and business insights

Machine Learning Engineers Opt focus on deploying scalable ML models in production environments, while Data Scientists primarily analyze data and develop models for insights. Both roles require strong technical skills, but their core responsibilities differ in application and deployment.

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

To thrive as a Machine Learning Engineer, you need a solid background in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch), data processing tools, and cloud platforms, along with relevant certifications, is highly valuable. Strong problem-solving ability, collaboration, and effective communication are standout soft skills in this role. These skills and qualities ensure the successful development, deployment, and integration of machine learning solutions that drive business value.

What are some common challenges Machine Learning Engineers face when deploying models to production environments?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, handling data drift, and integrating models seamlessly with existing systems when deploying to production. Monitoring model performance in real time and retraining models as new data becomes available are also critical tasks. Collaboration with data engineers and DevOps teams is essential to address infrastructure and deployment hurdles while maintaining model accuracy and reliability.
What are popular job titles related to Machine Learning Engineer Opt jobs in Colorado Springs, CO? For Machine Learning Engineer Opt jobs in Colorado Springs, CO, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Opt jobs in Colorado Springs, CO look for? The top searched job categories for Machine Learning Engineer Opt jobs in Colorado Springs, CO are:
Machine Learning Engineer Opt jobs near you

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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