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Deep Learning Quantization Jobs in Arizona (NOW HIRING)

Sr. Advanced AI Software Engineer

Phoenix, AZ · On-site

$121K - $160K/yr

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

Sr. Advanced AI Software Engineer

Phoenix, AZ · On-site

$121K - $160K/yr

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

Sr. Advanced AI Software Engineer

Laveen, AZ · On-site

$116K - $153K/yr

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

Sr. Advanced AI Software Engineer

Laveen, AZ · On-site

$115K - $152K/yr

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

Sr. Advanced AI Software Engineer

Glendale, AZ · On-site

$121K - $160K/yr

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

Sr. Advanced AI Software Engineer

Chandler, AZ · On-site

$120K - $159K/yr

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

Sr. Advanced AI Software Engineer

San Carlos, AZ · On-site

$121K - $160K/yr

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

Sr. Advanced AI Software Engineer

Goodyear, AZ · On-site

$119K - $157K/yr

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

Sr. Advanced AI Software Engineer

Phoenix, AZ · On-site

$116K - $153K/yr

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

Sr. Advanced AI Software Engineer

Peoria, AZ · On-site

$120K - $158K/yr

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

Sr. Advanced AI Software Engineer

Phoenix, AZ · On-site

$115K - $152K/yr

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

Sr. Advanced AI Software Engineer

Phoenix, AZ · On-site

$116K - $153K/yr

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

Sr. Advanced AI Software Engineer

Tempe, AZ · On-site

$117K - $154K/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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Showing results 1-20

Deep Learning Quantization information

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.

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 cities in Arizona are hiring for Deep Learning Quantization jobs? Cities in Arizona with the most Deep Learning Quantization job openings:
Sr. Advanced AI Software Engineer

Sr. Advanced AI Software Engineer

Honeywell

Phoenix, AZ • On-site

$121K - $160K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 20 days ago

Be an early applicant


Honeywell rating

8.3

Company rating: 8.3 out of 10

Based on 178 frontline employees who took The Breakroom Quiz

65th of 518 rated manufacturers


Job description

Job Description
Honeywell is seeking a Sr. Advanced AI Software Engineer to join our team! In this role, you will lead the strategic vision and roadmap while driving performance, reliability, and quality across our software products to support scalable business growth.
You will report directly to our Director of Engineering, and you'll work out of our Phoenix, Arizona location on a hybrid work schedule. Note: for the first 90 days, new hires must be prepared to work onsite 100% M-F.
Responsibilities
  • Design, build, and deploy advanced AI/ML systems (including LLMs and generative AI) end to end.
  • Architect scalable, reliable, and cost-efficient AI solutions for production environments.
  • Lead model optimization, evaluation, monitoring, and continuous improvement.
  • Drive MLOps practices for training, deployment, CI/CD, and lifecycle management.
  • Collaborate with product, data, and platform teams to deliver business-aligned AI outcomes.
  • Provide technical leadership through design reviews, mentorship, and AI strategy influence.

Qualifications
YOU MUST HAVE
  • 5+ years of hands-on AI/ML experience in production environments. Proven track record of delivering AI-powered systems at scale. Experience working in cross-functional, fast-paced environments
  • Core AI / Machine Learning
  • Deep expertise in: Machine learning fundamentals (supervised, unsupervised, reinforcement learning). Deep learning architectures (CNNs, RNNs, Transformers)
  • Hands-on experience with: Large Language Models (LLMs) and generative AI, Prompt engineering, fine-tuning, and RAG pipelines. Embeddings, vector databases, and semantic search.
  • Programming & Software Engineering: Strong proficiency in Python (primary AI development language), Experience with at least one additional language (Java, C++).
  • Solid understanding of: Data structures, algorithms, and distributed systems, API design, microservices, and backend integration, Ability to write clean, maintainable, production-quality code, AI & ML Frameworks.
  • Strong experience on one or more cloud platforms: Azure, AWS, Containerization and orchestration: Docker, Kubernetes: CI/CD tools for ML workflows

WE VALUE
  • Bachelor's degree in Computer Science, AI, ML, Data Science, or a related field
  • Master's degree (PhD is a Plus) in Computer Science, AI, ML, Data Science, or a related field
  • Multi-agent systems and autonomous agents
  • Edge AI or model compression/quantization
  • AI safety research and explainability techniques
  • Knowledge of: SQL and NoSQL databases
  • Familiarity with vector databases
  • Publications, patents, or open-source contributions in AI/ML

US PERSON REQUIREMENT
Due to compliance with U.S. export control laws and regulations, candidate must be a U.S. Person, which is defined as a U.S. citizen, a U.S. permanent resident, or have protected status in the U.S. under asylum or refugee status, or have the ability to obtain an export authorization.
BENEFITS OF WORKING FOR HONEYWELL
In addition to a competitive salary, leading-edge work, and developing solutions side-by-side with dedicated experts in their fields, Honeywell employees are eligible for a comprehensive benefits package. This package includes employer-subsidized Medical, Dental, Vision, and Life Insurance; Short-Term and Long-Term Disability; 401(k) match, Flexible Spending Accounts, Health Savings Accounts, EAP, and Educational Assistance; Parental Leave, Paid Time Off (for vacation, personal business, sick time, and parental leave), and 12 Paid Holidays.
ABOUT HONEYWELL
Honeywell International Inc. (Nasdaq: HON) invents and commercializes technologies that address some of the world's most critical challenges around energy, safety, security, air travel, productivity, and global urbanization. We are a leading software-industrial company committed to introducing state-of-the-art technology solutions to improve efficiency, productivity, sustainability, and safety in high-growth businesses in broad-based, attractive industrial end markets. Our products and solutions enable a safer, more comfortable, and more productive world, enhancing the quality of life of people around the globe.
The application period for the job is estimated to be 40 days from the job posting date; however, this may be shortened or extended depending on business needs and the availability of qualified candidates. Posting date: 4/28/2026
About Us
Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments - powered by our Honeywell Forge software - that help make the world smarter, safer and more sustainable.
Required Skills
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About Honeywell

Sourced by ZipRecruiter

Honeywell is charging into the Industrial IoT revolution with the establishment of Honeywell Connected Enterprise (HCE), building on our heritage of invention and deep, on-the-ground industry expertise. HCE is the leading industrial disruptor, building and connecting software solutions to streamline and centralize the assets, people and processes that help our customers make smarter, more accurate business decisions. Moving at the speed of software, we are creating, innovating and delivering solutions fast, challenging the way things have always been done, piloting new ways for all of us to work, and expecting our successes to set new standards for our customers and for Honeywell. The Chief Architect for Honeywell Connected Enterprise will lead a team of architects and system engineers responsible for the design of applications and infrastructure that deliver high value outcomes for customers in industrial, buildings, distribution centers, and aerospace vertical markets. The Chief Architect will work directly with leadership, development teams, and offering management to design well integrated solutions that utilize software platforming to encourage reuse and speed to market.

Industry

Furniture manufacturing

Company size

10,000+ Employees

Headquarters location

Charlotte, NC, US

Year founded

1906