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Embedded Machine Learning Jobs in Arizona (NOW HIRING)

Swarmbotics AI

Machine Learning Operations Engineer

Phoenix, AZ · On-site

$65K - $88K/yr

SwarmboticsAI is pushing the frontier of advanced machine learning models and architectures on edge ... Experience with edge AI deployment and embedded systems optimization * Prior robotics or autonomous ...

Axon

Senior Machine Learning Scientist

Scottsdale, AZ

$92K - $125K/yr

Your Impact We are seeking highly skilled and innovative Machine Learning Scientists to join our AI ... IoT devices, or embedded systems is highly desirable. * Excellent problem-solving skills ...

Axon

Senior Machine Learning Scientist

Scottsdale, AZ · On-site

$92K - $125K/yr

Your Impact We are seeking highly skilled and innovative Machine Learning Scientists to join our AI ... IoT devices, or embedded systems is highly desirable. * Excellent problem-solving skills ...

Atos

SOFTWARE ENGINEER

Phoenix, AZ

Design and maintain data pipelines for machine learning model training and inference. Architecture ... Here at Atos, diversity and inclusion are embedded in our DNA. Read more about our commitment to a ...

Atos

SOFTWARE ENGINEER

Phoenix, AZ · On-site

Design and maintain data pipelines for machine learning model training and inference. Architecture ... Here at Atos, diversity and inclusion are embedded in our DNA. Read more about our commitment to a ...

Advantest

Thermal Controls R&D Engineer

Chandler, AZ · On-site

$81K - $105K/yr

Support development of digital twins and predictive maintenance strategies using machine learning ... Hands-on experience with sensors, actuators, and embedded control hardware. * Excellent analytical ...

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All JobsEmbedded Machine Learning JobsEmbedded Machine Learning Jobs in Arizona

Embedded Machine Learning information

See Arizona salary details

$65.2K

$142.9K

$162.1K

How much do embedded machine learning jobs pay per year?

As of Jun 24, 2026, the average yearly pay for embedded machine learning in Arizona is $142,936.00, according to ZipRecruiter salary data. Most workers in this role earn between $122,500.00 and $161,200.00 per year, depending on experience, location, and employer.

Will AI replace embedded programmers?

Embedded machine learning involves developing algorithms for resource-constrained devices, and while AI tools can assist with coding and optimization, embedded programmers are essential for designing, implementing, and maintaining these systems. AI is more likely to augment their work rather than fully replace them, especially given the need for specialized knowledge of hardware and real-time constraints.

Is embedded AI a good career?

Embedded machine learning involves developing AI models for hardware with limited resources, such as IoT devices and embedded systems. It is a growing field with demand for skills in hardware programming, C/C++, and AI frameworks, offering opportunities in industries like automotive, healthcare, and consumer electronics.

Is embedded systems still a good career in 2026?

Embedded Machine Learning remains a strong career in 2026 as industries increasingly adopt AI-powered devices and IoT solutions. Professionals with skills in hardware programming, real-time systems, and machine learning frameworks like TensorFlow Lite are in demand for developing intelligent embedded applications. Continuous learning and familiarity with microcontrollers, sensors, and embedded software development are essential for long-term growth in this field.

What engineers make $500,000?

Senior engineers in specialized fields such as embedded machine learning, AI, or data science can reach salaries of $500,000 or more, especially with extensive experience, advanced skills in programming and hardware, and leadership roles. High compensation often involves working in high-demand industries, with additional bonuses or stock options contributing to total earnings.

What are some common challenges faced by professionals working in embedded machine learning roles?

Professionals in embedded machine learning roles often face the challenge of optimizing machine learning models to run efficiently on resource-constrained hardware, such as microcontrollers or edge devices with limited memory and processing power. Balancing model accuracy, inference speed, and energy consumption can require creative problem-solving and deep knowledge of both hardware and software. Additionally, collaboration with hardware engineers, data scientists, and software developers is key, as projects typically require cross-functional teamwork to meet performance and deployment goals. Staying current with rapidly evolving tools and best practices is also important in this dynamic field.

What is an Embedded Machine Learning job?

An Embedded Machine Learning job involves developing and optimizing machine learning models to run efficiently on resource-constrained devices like microcontrollers, edge devices, and IoT hardware. Professionals in this role work on model compression, low-power inference, and real-time processing, ensuring AI capabilities can function without relying on cloud computing. Responsibilities often include data preprocessing, feature extraction, model training, and deployment on embedded systems using frameworks like TensorFlow Lite or Edge Impulse.

What are the key skills and qualifications needed to thrive in the Embedded Machine Learning position, and why are they important?

To thrive in Embedded Machine Learning, you should have expertise in machine learning algorithms, embedded systems programming (e.g., C/C++, Python), and a solid understanding of hardware-software integration, typically backed by a degree in computer engineering, electrical engineering, or a related field. Familiarity with edge AI tools (such as TensorFlow Lite, ONNX, or Edge Impulse), microcontrollers, and real-time operating systems is highly valued, alongside relevant certifications such as Embedded Systems or AI certificates. Strong problem-solving skills, effective communication, and the ability to work cross-functionally are crucial soft skills in this field. These qualifications and qualities are vital for creating efficient, reliable AI solutions that operate seamlessly within resource-constrained environments and interdisciplinary project teams.

What are the most commonly searched types of Embedded Machine Learning jobs in Arizona? The most popular types of Embedded Machine Learning jobs in Arizona are:
What are popular job titles related to Embedded Machine Learning jobs in Arizona? For Embedded Machine Learning jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Embedded Machine Learning jobs in Arizona look for? The top searched job categories for Embedded Machine Learning jobs in Arizona are:
What cities in Arizona are hiring for Embedded Machine Learning jobs? Cities in Arizona with the most Embedded Machine Learning job openings:
Embedded Machine Learning jobs near you
Infographic showing various Embedded Machine Learning job openings in Arizona as of June 2026, with employment types broken down into 79% Full Time, 9% Part Time, 4% Temporary, 4% Contract, and 4% Nights. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $142,936 per year, or $68.7 per hour.

Machine Learning Operations Engineer

Swarmbotics AI

Phoenix, AZ • On-site

$65K - $88K/yr

Full-time

Posted 9 days ago

Job description

Company Background:
SwarmboticsAI is pushing the frontier of advanced machine learning models and architectures on edge devices for swarms of unmanned ground vehicles (UGV). We see an urgent need for low-cost intelligent autonomous swarm UGV systems in the defense space. Our primary product is a defense application of swarm 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 advanced and coordinated high-level capabilities across multiple domains.
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.
Position description:
SwarmboticsAI is seeking a highly skilled MLOps Engineer to design, build, and maintain the machine learning infrastructure that powers our autonomous swarm systems. This engineer will be responsible for creating robust, scalable ML pipelines that support our perception team's cutting-edge computer vision and deep learning models. You'll ensure seamless model training, deployment, and monitoring across our fleet of UGVs. This engineer will work closely with our ML/Perception team and company leadership to scale our ML capabilities across the SwarmboticsAI product roadmap.
What you'll do:
  • Design and implement end-to-end ML pipelines for training, validation, and deployment of perception models
  • Develop robust data management systems for large-scale sensor data (cameras, LiDAR, IMU) collected from field operations
  • Implement model monitoring, A/B testing, and performance tracking systems for deployed models
  • Build CI/CD pipelines for model versioning, testing, and deployment to vehicle fleets
  • Design distributed computing solutions for large-scale data processing and model training
  • Create tools for data annotation, model evaluation, and performance visualization
  • Work collaboratively with perception engineers, robotics teams, and field operations

Required qualifications:
  • Minimum 2 years industry experience in MLOps, DevOps, or ML infrastructure
  • Bachelor's degree in computer science, engineering, or related field
  • Strong experience with ML pipeline orchestration tools (Kubeflow, MLflow, or similar)
  • Proficiency in containerization (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure)
  • Strong Python programming and Linux system administration skills
  • Experience with model serving frameworks (TensorRT, ONNX Runtime, TorchServe)
  • Knowledge of data versioning and experiment tracking (Weights & Biases, Neptune, or similar)
  • Experience with monitoring and logging systems (Prometheus, Grafana, ELK stack)
  • Strong organization and communication to work well across teams in a fast-paced startup environment
  • Comfort working in the high-paced, fluid environment of a tech startup
  • Excitement about contributing to the defense of the United States and its allies
  • Must be eligible to work on export-controlled projects.
  • Ability to relocate to Phoenix, AZ area

Nice to have qualifications:
  • Masters degree in computer science, engineering, or related field
  • Experience with edge AI deployment and embedded systems optimization
  • Prior robotics or autonomous vehicle MLOps experience
  • Experience with real-time data streaming (Kafka, RabbitMQ)
  • Knowledge of security and compliance requirements for defense applications
  • Experience with multi-modal sensor data processing and fusion
  • Familiarity with ROS and robotics software stacks
  • Experience with a CatBs framework is preferred

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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