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Deep Learning Quantization Jobs in Phoenix, AZ (NOW HIRING)

Honeywell

Sr. Advanced AI Software Engineer

Phoenix, AZ

$121.60K - $160.40K/yr

Core AI / Machine Learning * Deep expertise in : Machine learning fundamentals (supervised ... Edge AI or model compression/quantization * AI safety research and explainability techniques

Honeywell

Sr. Advanced AI Software Engineer

Phoenix, AZ · On-site

$121.60K - $160.40K/yr

Core AI / Machine Learning * Deep expertise in : Machine learning fundamentals (supervised ... Edge AI or model compression/quantization * AI safety research and explainability techniques

Honeywell

Sr. Advanced AI Software Engineer

Phoenix, AZ · On-site

$115.60K - $152.40K/yr

Core AI / Machine Learning * Deep expertise in : Machine learning fundamentals (supervised ... Edge AI or model compression/quantization * AI safety research and explainability techniques

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All JobsDeep Learning Quantization JobsDeep Learning Quantization Jobs in Phoenix, AZ

Deep Learning Quantization information

See Phoenix, AZ salary details

$10.9K

$83.3K

$139K

How much do deep learning quantization jobs pay per year?

As of May 28, 2026, the average yearly pay for deep learning quantization in Phoenix, AZ is $83,291.00, according to ZipRecruiter salary data. Most workers in this role earn between $71,500.00 and $138,000.00 per year, depending on experience, location, and employer.

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 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 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 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.

What are popular job titles related to Deep Learning Quantization jobs in Phoenix, AZ? For Deep Learning Quantization jobs in Phoenix, AZ, the most frequently searched job titles are:
What job categories do people searching Deep Learning Quantization jobs in Phoenix, AZ look for? The top searched job categories for Deep Learning Quantization jobs in Phoenix, AZ are:
What cities near Phoenix, AZ are hiring for Deep Learning Quantization jobs? Cities near Phoenix, AZ with the most Deep Learning Quantization job openings:
Deep Learning Quantization jobs near you
Infographic showing various Deep Learning Quantization job openings in Phoenix, AZ as of May 2026, with employment types broken down into 89% Full Time, and 11% Contract. Highlights an 68% In-person, 6% Hybrid, and 26% Remote job distribution, with an average salary of $83,291 per year, or $40 per hour.

Senior / Staff ML Training Optimization Engineer

Waabi

Phoenix, AZ • On-site, Remote

$141K - $249K/yr

Full-time

Medical, Dental, Vision, PTO

Posted 20 days ago

Job description

Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.

With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai

You will...
- Build standardized distributed training frameworks for research and production, drive our training towards new levels of stability and efficiency.
- Comprehensively profile model runtime and memory to pinpoint performance bottlenecks.
- Identify and evaluate emerging technologies that can be adopted into Waabi's training and inference frameworks. Examples include designing new CUDA kernels, quantization-aware training and inference, and compilation/deployment techniques.
- Work with researchers and ML engineers on best-practices for optimal resource usage.
- Create and improve tooling and dashboards to ensure broad adoption of your work.
 
Qualifications:
- MS/PhD or Bachelors degree with a minimum of 4 years of industry experience in Computer Science, Robotics and/or similar technical field(s) of study.
- Solid coding proficiency in a variety of coding languages including Python, C++ or Rust.
- Experience in deep learning frameworks such as PyTorch or Jax.
- Skilled in profiling CPU and GPU code using tools such as PyTorch Profiler and NVIDIA Nsight.
- Open-minded and collaborative team player with willingness to help others.
- Passionate about self-driving technologies, solving hard problems, and creating innovative solutions.
 
Bonus/nice to have:
- Experience in identifying when custom CUDA kernels are needed, and implementing them.
- Experience in Bazel in a monorepo environment, and integrating third party packages into dev environments.
- Experience with Kubernetes-based training platforms.
 
The US yearly salary range for this role is: $141,000 - $249,000 in addition to competitive perks & benefits. Waabi's yearly salary ranges are determined based on several factors in accordance with the Company's compensation practices. The salary base range is reflective of the minimum and maximum target for new hire salaries for the position across all US locations.  Note: The Company provides additional compensation for employees in this role, including equity incentive awards and an annual performance bonus.

Perks/Benefits:
Waabi provides a competitive benefits package that includes:
- Competitive compensation and equity awards.
- Health and Wellness benefits encompassing Medical, Dental and Vision coverage.
- Unlimited Vacation.
- Flexible hours and Work from Home support.
- Daily drinks, snacks and catered meals (when in office).
- Regularly scheduled team building activities and social events both on-site, off-site & virtually.
- World-class facility that includes a gym, games room (ping pong table, video game consoles, board games, etc), multiple collaborative working spaces and a gorgeous patio!(when in office)
- As we grow, this list continues to evolve! 

Waabi is an equal opportunity employer that celebrates diversity and is committed to creating a supportive, inclusive, and accessible environment for all employees. If reasonable accommodation is needed to participate in the job application or interview process please let our recruiting team know.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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