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Machine Learning Engineer Quantization Jobs in Arizona

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AI & Machine Learning Engineer

Chandler, AZ · On-site

$100K - $110K/yr

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Knowledge of Machine Learning and Generative AI frameworks * Strong engineering fundamentals and problem-solving skills * A builder mindset -- curious, resourceful, fast-moving, and focused on ...

Programmers.io

AI/ML Engineer

Scottsdale, AZ · On-site

Google Cloud Professional Machine Learning Engineer Google Cloud Professional Data Engineer AWS Certified Machine Learning Specialty Certified Kubernetes Admin(CKA) Google Professional Cloud ...

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Machine Learning Engineer Quantization information

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.

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Machine Learning Engineer Quantization jobs near you
Infographic showing various Machine Learning Engineer Quantization job openings in Arizona as of June 2026, with employment types broken down into 69% Full Time, 23% Part Time, 4% Temporary, and 4% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Machine Learning Engineer with Security Clearance

Prime Solutions Group, Inc

Goodyear, AZ • On-site

$110K/yr

Other

Re-posted yesterday

Job description

Turn machine learning into real-world mission capability.
PSG is seeking a Machine Learning Engineer to design, build, and deploy AI/ML solutions that power mission-critical systems. This role focuses on taking models from concept to production—developing pipelines, integrating models into software systems, and ensuring performance, scalability, and reliability in real-world environments. You’ll work at the intersection of machine learning, software engineering, and DevSecOps, collaborating with cross-functional teams to deliver secure, production-ready AI solutions supporting national security missions. What You’ll Do
- Design, build, and maintain ML pipelines for data preparation, training, evaluation, and deployment - Develop and optimize ML models and applications using Python and frameworks like PyTorch or TensorFlow - Integrate models into production systems (APIs, batch pipelines, real-time services) - Implement model validation, evaluation metrics, and performance monitoring - Improve model accuracy, scalability, and efficiency through tuning and data strategy improvements - Collaborate with data engineers and domain experts to prepare and validate datasets - Partner with DevSecOps/MLOps teams to deploy ML solutions in secure environments - Troubleshoot model and pipeline issues; perform root cause analysis and optimization - Contribute to technical documentation, test plans, and operational runbooks - Participate in design reviews, architecture discussions, and Agile development processes - Mentor junior engineers and promote engineering best practices Requirements
- U.S. Citizenship - Active Top Secret Clearance (SCI eligibility; CI Poly preferred or ability to obtain) - Bachelor’s degree in Computer Science, Engineering, Data Science, or related field - 4+ years of experience in: - Machine Learning Engineering - Applied AI/ML development - Production ML systems - Strong Python skills and experience with ML libraries (NumPy, pandas, scikit-learn, PyTorch, TensorFlow) - Experience developing, training, and deploying ML models in real-world applications - Solid understanding of the ML lifecycle (data > training > validation > deployment > monitoring) - Experience building maintainable, production-quality software - Familiarity with Docker and cloud environments (AWS, Azure, or GCP) - Experience working in Agile and CI/CD environments - Strong problem-solving, communication, and collaboration skills Preferred Qualifications
- Master’s degree in a related field - Experience with computer vision, image/video analytics, or sensor data (e.g., RF, SAR) - Experience transitioning models from research to production environments - Familiarity with experiment tracking, model versioning, and reproducibility practices - Experience with GPU-based ML workflows and cloud ML platforms - Background in defense, intelligence, or other regulated environments Why Join PSG?
At PSG, you’re not just taking a job—you’re building technology that matters.
- Competitive compensation & benefits - 9/80 flexible work schedule - Professional development & tuition assistance - Small, agile team with high ownership and visibility - Work on mission-critical systems supporting national security - Opportunities to grow across AI/ML, software engineering, and platform development Bring your machine learning expertise to PSG and help deliver the next generation of secure, intelligent, mission-driven systems. Salary Description
Salary range starts at $110,000 with the potential for higher compensation based on experience, skills, and mission needs.

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