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Machine Learning Engineer Quantization Jobs in Phoenix, AZ

Axon

Senior Machine Learning Scientist

Scottsdale, AZ

$92K - $125K/yr

What You'll Do Location: any cities with Axon Engineering Hub in US, Vietnam, EU (see * US: Seattle ... in computer vision, machine learning, and deep learning, MLLMs, GenAI and integrate relevant ...

Axon

Senior Machine Learning Scientist

Scottsdale, AZ · On-site

$92K - $125K/yr

What You'll Do Location: any cities with Axon Engineering Hub in US, Vietnam, EU (see * US: Seattle ... in computer vision, machine learning, and deep learning, MLLMs, GenAI and integrate relevant ...

Deloitte

AI Solutions Architect

Tempe, AZ · On-site

$60.25 - $79.50/hr

Certifications in artificial intelligence, machine learning, or cloud platforms, such as AWS Certified Machine Learning - Specialty, Google Cloud Professional Machine Learning Engineer, Microsoft ...

Honeywell

Sr. Advanced AI Software Engineer

Phoenix, AZ

$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

Honeywell

Sr. Advanced AI Software Engineer

Phoenix, AZ

$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

Honeywell

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

KUBRA

AI Engineer

Tempe, AZ · Hybrid

The AI Engineer is responsible for designing, building, and operationalizing intelligent systems ... Establish and enforce AI and machine learning and data operational standards, governance, and best ...

KUBRA

AI Engineer

Tempe, AZ · On-site

The AI Engineer is responsible for designing, building, and operationalizing intelligent systems ... Establish and enforce AI and machine learning and data operational standards, governance, and best ...

KUBRA

AI Engineer

Tempe, AZ · Hybrid

The AI Engineer is responsible for designing, building, and operationalizing intelligent systems ... Establish and enforce AI and machine learning and data operational standards, governance, and best ...

Staffingine LLC

AIML Engineer

Scottsdale, AZ · On-site

Experience implementing and supporting endtoend Machine Learning workflows and patterns * Expert level programming skills in Python and experience with Data Science and ML packages and frameworks

Diligent Tec, Inc.

AI/ML Engineer

Scottsdale, AZ

Role: AI/ML Engineer Location: Scottsdale, AZ (Hybrid) Duration: Full Time We are seeking an ... Responsibilities: • Machine Learning Development: o Design, develop, and implement machine ...

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All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in Phoenix, AZ

Machine Learning Engineer Quantization information

See Phoenix, AZ salary details

$31.3K

$127.9K

$192.1K

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

As of Jun 8, 2026, the average yearly pay for machine learning engineer quantization in Phoenix, AZ is $127,856.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,800.00 and $153,900.00 per year, depending on experience, location, and employer.

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.

What are popular job titles related to Machine Learning Engineer Quantization jobs in Phoenix, AZ? For Machine Learning Engineer Quantization jobs in Phoenix, AZ, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Quantization jobs in Phoenix, AZ look for? The top searched job categories for Machine Learning Engineer Quantization jobs in Phoenix, AZ are:
Machine Learning Engineer Quantization jobs near you
Infographic showing various Machine Learning Engineer Quantization job openings in Phoenix, AZ as of May 2026, with employment types broken down into 44% Full Time, 30% Part Time, and 26% Summer. Highlights an 72% In-person, and 28% Hybrid job distribution, with an average salary of $127,856 per year, or $61.5 per hour.

Swarm Engineer - Multi-Agent Task Planning

Swarmbotics AI

Phoenix, AZ • On-site

Full-time

Posted 11 days ago

Job description

Company background
Swarmbotics AI is a low-cost, swarm robotics company for industry and defense. We see a world of ubiquitous low-cost robots transforming almost all aspects of society, but we see an urgent need in the defense industry. We focus on building swarms of robots that incorporate a low-cost BOM, an autonomous stack optimized for off the shelf components, and a global planner that enables swarm capabilities for groups of robots to accomplish sophisticated tasks.
Our first product is a defense application building Unmanned Ground Vehicles (UGVs), collectively termed - Attritable, Networked, Tactical Swarm (ANTS). Each UGV in ANTS is an independently-tasked, attritable robot designed for on-demand and autonomous mobility. When operating as a swarm, ANTS is capable of executing more advanced and coordinated, high-level capabilities across a battlespace. ANTS will help solve some of the DoD's biggest problems that will save lives and increase defense capabilities.
Stephen Houghton and Drew Watson are the Founders and have decades of experience in self-driving cars and trucks, humanoids, and UAVs with experience from NASA, JPL, Cruise, Embark, McKinsey, Amazon, and the CIA.
Job description
Swarmbotics AI is seeking a Machine Learning Engineer to design, develop, and deploy a **multi-modal action model** that enables each UGV to select and execute coordinated swarm macro-actions in real time. This role sits at the intersection of machine learning and multi-agent decision making: you will build learned models that reason over multi-modal inputs to perform tactical macro-actions.
This is not a perception role. The core focus is on the decision-making and action-selection layers - training models that translate situational awareness into intelligent swarm behavior. You will work closely with company leadership and cross-functional teams to align capabilities with the Swarmbotics AI product roadmap.
What You'll Do
  • Architect, train, and iterate on multi-modal action models that select swarm-level tactical macro-actions from rich contextual inputs
  • Design model architectures that fuse heterogeneous inputs - local perception, swarm state, mission objectives - into a unified decision representation
  • Develop and apply online and offline reinforcement learning approaches, including transformer-based sequence modeling, to learn swarm coordination policies
  • Optimize models to run real-time on edge devices through quantization, distillation, and efficient architecture design
  • Build and maintain the full pipeline from data collection and curation through training, evaluation, and field deployment
  • Integrate the action model into the broader autonomy stack alongside navigation, planning, and swarm coordination subsystems
  • Deploy and validate trained models on physical UGV swarms in field environments
  • Write robust Python and C++ code

Required qualifications
  • Strong mathematical foundation in neural networks, transformers, reinforcement learning, and statistics
  • Proficiency in Python and C++
  • Experience with PyTorch or TensorFlow
  • Experience training and deploying models that produce **actions or macro-actions** (e.g., online or offline reinforcement learning, planning-as-inference, VLA models, or similar) - not solely classification or perception
  • Familiarity with multi-agent coordination concepts: task allocation, distributed decision-making, or swarm behaviors
  • Experience optimizing and deploying ML models on resource-constrained or edge hardware

Preferred qualifications
  • Hands-on experience with policy gradient methods such as PPO
  • Experience with multi-agent task planning algorithms (task allocation, scheduling, auction-based methods)
  • Familiarity with ONNX, TensorRT, and edge deployment toolchains
  • Prior robotics experience, autonomous driving background, or work with unmanned systems
  • Experience with simulation environments and synthetic data generation for training multi-agent policies
  • Experience owning an entire data-to-production model pipeline
  • Academic publications in related fields (e.g., NeurIPS, AAAI, IROS, ICRA, JAIR)
  • Experience with a CatBs framework is preferred but not required

The preceding description is not designed to be a complete list of all duties and responsibilities required for the position. Swarmbotics is an equal-opportunity employer. All qualified applicants will be treated with respect and receive equal consideration for employment without regard to race, color, caste, creed, religion, sex, gender identity, sexual orientation, national origin, ancestry, disability, uniform service, Veteran status, age, or any other protected characteristic per federal, state, or local law.
The preceding description is not designed to be a complete list of all duties and responsibilities required for the position. Swarmbotics is an equal-opportunity employer. All qualified applicants will be treated with respect and receive equal consideration for employment without regard to race, color, caste, creed, religion, sex, gender identity, sexual orientation, national origin, ancestry, disability, uniform service, Veteran status, age, or any other protected characteristic per federal, state, or local law.

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