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Deep Learning Quantization Jobs in Colorado (NOW HIRING)

AppFolio

CO · On-site

Production experience with model serving for both LLMs and custom models; understands quantization ... Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation ...

AppFolio

CO · On-site

Production experience with model serving for both LLMs and custom models; understands quantization ... Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation ...

Judge Group, Inc.

Data Science Engineer

Westminster, CO · On-site

$100K - $140K/yr

... quantization, pruning, or architectural simplifications that meet deployment constraints ... Fluency in at least one deep-learning framework (PyTorch is strongly preferred). Demonstrated ...

Pwc

AI Engineer

Denver, CO · On-site

$50K - $112K/yr

... networks and deep learning methods for advanced AI applications - Managing data quality and ... using quantization, inference acceleration, and model-routing techniques - Designing agent ...

All JobsDeep Learning Quantization JobsDeep Learning Quantization Jobs in Colorado

Deep Learning Quantization information

What is deep learning quantization?

Deep learning quantization is the process of reducing the precision of the numbers used to represent a neural network's parameters, activations, or both. By converting the typically used 32-bit floating-point values to lower bit-width formats such as 16-bit or 8-bit integers, quantization significantly reduces the memory footprint and computational requirements of deep learning models. This technique helps deploy models efficiently on edge devices and mobile hardware while maintaining acceptable accuracy levels. Quantization is widely used in model optimization for faster inference and lower power consumption.

What are some common challenges faced when implementing deep learning quantization in production environments?

One of the main challenges in implementing deep learning quantization is balancing model accuracy with computational efficiency, as quantization can sometimes lead to a drop in model performance. Additionally, ensuring hardware compatibility and optimizing for different devices (such as CPUs, GPUs, or edge devices) can require extensive testing and tuning. Collaboration with data scientists, software engineers, and hardware specialists is often essential to successfully deploy quantized models at scale. Staying updated with the latest quantization techniques and frameworks is also important for overcoming these challenges.

What are the key skills and qualifications needed to thrive as a deep learning quantization engineer, and why are they important?

To excel as a Deep Learning Quantization Engineer, you need a strong background in machine learning, applied mathematics, and computer science, usually supported by an advanced degree in a related field. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), quantization toolkits, and hardware acceleration platforms is crucial. Analytical thinking, problem-solving, and clear technical communication are standout soft skills in this role. These abilities are essential for efficiently optimizing models for deployment on resource-constrained hardware while maintaining accuracy and performance.

What is the difference between Deep Learning Quantization vs Machine Learning Engineer?

AspectDeep Learning QuantizationMachine Learning Engineer
Required CredentialsAdvanced degrees in AI, Computer Science, or related fields; knowledge of neural networksBachelor's or Master's in CS, Data Science, or related fields; programming skills
Work EnvironmentResearch labs, AI development teams, hardware optimization settingsSoftware development teams, data-driven projects, product-focused environments
Industry UsageAI hardware optimization, model deployment, edge computingModel development, data analysis, software solutions across industries

Deep Learning Quantization focuses on reducing model size and improving inference speed through techniques like weight and activation quantization, often in hardware or embedded systems. Machine Learning Engineers develop, implement, and optimize machine learning models for various applications. While both roles require knowledge of AI and programming, Deep Learning Quantization is more specialized in model optimization techniques, whereas Machine Learning Engineers work broadly on model development and deployment.

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Deep Learning Quantization jobs near you
Infographic showing various Deep Learning Quantization job openings in Colorado as of June 2026, with employment types broken down into 2% As Needed, 30% Full Time, 61% Part Time, and 7% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution.

Machine Learning Engineer

AMP: AI-Powered Sortation for Waste and Recycling

Louisville, CO • On-site, Remote

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 5 days ago

Job description

AMP is applying AI-powered sortation at scale to modernize the world's recycling infrastructure and maximize the value in waste. AMP gives waste and recycling leaders the power to harness AI to reduce labor costs, increase resource recovery, and deliver more reliable operations. With hundreds of deployments across North America, Asia, and Europe, AMP's technology offers a transformational solution to waste sortation and changes the fundamental economics of recycling.

Headquartered in Louisville, Colorado, the Denver Post and BuiltIn Colorado have recognized AMP as one of the state's top workplaces. AMP has operations and career opportunities outside of Atlanta, Cleveland, Portsmouth, Virginia, and Europe. We're fostering an environment where passionate individuals can grow and create impact. We seek unconventional thinkers to join our mission to enable a world without waste; at AMP, your contributions have meaning and can spur change. With backing from top-tier investors and national recognition including North American Cleantech Company of the Year, we're always seeking ways to better our operations, raising the bar on innovation, and looking to collaborate and improve in what we do. Learn more at AMPSortation.com. 

AMP Robotics is hiring a Machine Learning Engineer reporting to the Engineering Manager of Perception to focus on developing our deep learning and other machine learning models and shipping them to production. AMP doesn't just do machine learning, we are driven forward by it. Our core technology revolves around deep-learned models applied to robotics domains, and we are always striving to improve our performance. In pursuit of this, AMP is looking for a highly skilled individual to join our machine learning team and aid in building out cutting edge computer vision technology. In this role, you would be an individual contributor on our perception team, working on deep learning and computer vision research and development projects to help us implement state of the art deep learning techniques, and helping to develop new applications by working hands-on with data, modeling, and evaluation. You would aid in bringing models from ideation all the way through to production, and help us to maintain our technological edge.

As our Machine Learning Engineer, you will work to:

  • Experiment with modern neural network architectures or techniques driven by research publications.
  • Design and implement machine learning perception solutions in new domains.
  • Design and execute experiments to validate R&D deep learning approaches.
  • When experiments have positive results, design a path to production to deliver impact to our facilities.
  • Collaborate with AI Data Team project managers, ML modeling engineers, and Cloud infrastructure engineers.
  • Serve as a subject matter expert for experiment design and statistical modeling for the Software organization, applying these skills to non deep learning projects when relevant.
  • Help us support and improve our ML infrastructure.

The successful candidate will have:

Required:

  • Master's degree in Computer Science/Machine Learning or similar, or equivalent combination of technical education and work experience.
  • 4+ years' experience writing production-level code in python.
  • 4+ years of experience implementing statistical models in production environments.
  • Proficiency with professional software engineering practices; including coding standards, code reviews, source control management, build processes, testing, and operations.
  • Strong familiarity with PyTorch.
  • Strong familiarity with experiment design and experiment execution.
  • Research fluency with a deep learning domain.

Preferred: 

  • 3+ years of experience with deep learning, particularly computer vision.
  • Proficiency with neural network quantization, acceleration, and inference tools, particularly TensorRT.
  • Familiarity and interest in research-level mathematics.
  • Proficiency working with SQL databases and data pipelines.
  • Experience deploying large-scale and small-scale models in low-latency environments.
  • Startup ready mentality.
  • Passion for recycling, robotics and changing the world.

Working Conditions/Physical Demands:

The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job.  Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. 

  • Prolonged periods of sitting at a desk and working on a computer.
  • Must be able to lift up to 15 pounds at a time.

Remote or Hybrid: 

  • Full-remote with regular travel to AMP's Louisville CO HQ or
  • Hybrid or full time in-office at AMP's Louisville CO HQ

AMP provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Applicants who identify with a historically underrepresented group are encouraged to apply. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

Other duties: 

Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities, and activities may change at any time with or without notice.

We recognize that there is more to work than the day-to-day responsibilities. In addition to a collaborative, high-performing team environment, we're pleased to offer competitive base salaries; medical, dental and vision insurance; a 401(k) plan; paid time off and sick time; flexible work hours; and the opportunity to quickly accelerate your learning and growth.

Salary & Compensation information: $162,000 to $170,000 

Benefits information:

  • Medical - The company covers up 78% to 100% of the premium for Cigna healthcare plans depending on the selection. Employees pay the difference in premium if they select a more expensive plan. Up to 75% for dependents. 
  • Dental, Vision, Short- and Long-Term Disability.
  • 401(k) retirement plan (non-matching).
  • Flexible Time Off
  • 6 paid sick days.
  • Eight (8) paid holidays 

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