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Embedded Machine Learning Jobs in Arizona (NOW HIRING)

Software Engineer II

Tucson, AZ · On-site

$92K - $126K/yr

... time systems, machine learning, cybersecurity, and DevOps. Join our team of creative problem ... Experience with hardware-software integration and embedded system testing. * Active and ...

Software Engineer II

Tucson, AZ

$92K - $126K/yr

... time systems, machine learning, cybersecurity, and DevOps. Join our team of creative problem ... Experience with hardware-software integration and embedded system testing. * Active and ...

Software Engineer II

Phoenix, AZ

$96K - $132K/yr

... time systems, machine learning, cybersecurity, and DevOps. Join our team of creative problem ... Experience with hardware-software integration and embedded system testing. * Ability to obtain ...

Software Engineer II

Tucson, AZ · On-site

$92K - $126K/yr

... time systems, machine learning, cybersecurity, and DevOps. Join our team of creative problem ... Experience with hardware-software integration and embedded system testing. * Ability to obtain ...

Software Engineer II

Tucson, AZ

$92K - $126K/yr

... time systems, machine learning, cybersecurity, and DevOps. Join our team of creative problem ... Experience with hardware-software integration and embedded system testing. * Ability to obtain ...

Software Engineer II

Phoenix, AZ

$96K - $132K/yr

... time systems, machine learning, cybersecurity, and DevOps. Join our team of creative problem ... Experience with hardware-software integration and embedded system testing. * Active and ...

Software Engineer II

Tucson, AZ

$92K - $126K/yr

... time systems, machine learning, cybersecurity, and DevOps. Join our team of creative problem ... Experience with hardware-software integration and embedded system testing. * Active and ...

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Showing results 1-20

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:
Infographic showing various Embedded Machine Learning job openings in Arizona as of June 2026, with employment types broken down into 87% Full Time, and 13% Part Time. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $142,936 per year, or $68.7 per hour.

VP, Talent & Organizational Development

Cognite - AI for Industry

Phoenix, AZ

Other

Medical, Dental, Vision, Retirement, PTO

Posted 13 days ago


Job description

How you'll demonstrate Ownership

The VP of Talent & Organizational Development is a transformative executive role for a leader who will architect how Cognite builds human capability at scale, in an era where the boundary between human expertise and AI augmentation is being redrawn in real time.

This role demands a rare profile: a strategic People leader with deep conviction about where AI is taking the workforce, the technical literacy to engage credibly, and the organizational design expertise to build structures that thrive. You will lead the functions of Talent, Leadership Development, Learning, Succession, and Organizational Effectiveness - transforming each through AI-powered insight, tooling, and methodology.

Talent Strategy 

  • Define Cognite's global talent strategy - identifying where human capability must lead, where AI augments, and how the workforce of 2030 needs to be built today.
  • Deploy AI-driven talent intelligence to map external talent markets, predict attrition risk, and identify capability gaps before they become business constraints.

AI-Augmented Leadership & Organizational Development

  • Design leadership development programs explicitly built for an AI-augmented world - developing leaders who can direct, collaborate with, and critically evaluate AI systems, not just manage people.
  • Introduce AI-powered coaching tools and continuous feedback platforms that give leaders real-time insight into their effectiveness and accelerate behavioral development at scale.
  • Build succession planning frameworks powered by predictive analytics - moving from intuition-based talent reviews to data-driven readiness assessments with quantified risk and opportunity scoring.
  • Drive organizational design initiatives - reimagining team structures, role definitions, and performance models for a world where AI agents are increasingly embedded in workflows.

AI-Enabled Learning & Capability Building

  • Architect a next-generation learning ecosystem powered by AI - delivering personalized development paths that adapt to individual capability gaps, career goals, and real-time performance data.
  • Integrate AI tutors, simulation environments, and intelligent practice tools into Cognite's learning infrastructure, enabling employees to build skills faster and with greater retention.
  • Create structured reskilling and upskilling programs anticipating AI-driven role displacement and evolution - transforming potential workforce disruption into a retention and development opportunity.

Culture, Engagement & Human-AI Inclusion

  • Champion a culture of human potential building psychological safety and growth mindsets that allow people to embrace AI as a collaborator.
  • Position Cognite as a model of responsible AI adoption in the workplace - developing clear principles, communication strategies, and change programs that build trust in AI-powered people processes.

Talent Intelligence & Workforce Analytics

  • Build a world-class people analytics capability, transforming raw HR data into predictive workforce intelligence that drives strategic business decisions.
  • Develop dashboards and talent scorecards that give the executive team real-time visibility into leadership readiness, capability depth, engagement risk, and workforce trajectory.
  • Use machine learning models to forecast hiring needs, succession gaps, retention risks, and skill obsolescence - enabling proactive rather than reactive talent decisions.
The Impact you bring to Cognite
  • A visionary People leader with genuine conviction about the transformative impact of AI on human capability, talent strategy, and organizational design.
  • Technical literacy that enables you to engage credibly with AI practitioners, evaluate tools critically, and distinguish genuine innovation from AI theater.
  • A proven systems architect - able to connect talent strategy, AI tooling, organizational design, and culture into an integrated, self-reinforcing people architecture.
  • Exceptional executive presence, with the credibility to lead the workforce transformation conversation at the most senior levels of a global AI company.
  • A builder's mindset: as comfortable stress-testing a workforce planning model as you are presenting a board-level talent strategy.
  • Relentless curiosity - someone who reads the research, experiments with new tools, and brings the outside in to keep Cognite's talent practices at the frontier.
 
Required Qualifications
  • 15+ years of progressive experience in talent management, organizational development, or related HR leadership roles, with significant exposure to technology-driven transformation.
  • At least 4-5 years in a VP-level or equivalent senior leadership role in a high-growth global technology company.
  • Strong command of people analytics tools and platforms, with the ability to translate data into executive-level strategic recommendations.
  • Experience navigating rapid organizational scaling, including workforce redesign in the context of automation and AI-driven role evolution.
  • Location: Hybrid, based in Phoenix, Arizona

Preferred Experience

  • Experience in SaaS, AI, or industrial technology environments - familiarity with engineering and technical talent markets is a strong plus.
  • Background in companies navigating high-growth transitions or pre-IPO organizational maturation.
  • Experience in industrial technology, SaaS, or AI-native companies - with familiarity in attracting and retaining engineering and applied science talent.
  •  Advanced degree (MBA, MS Organizational Psychology, MS Human-Computer Interaction, or similar) or relevant certification (SHRM-SCP, CIPD Level 7, ICF).
A snapshot of our many perks and benefits as a Cogniter
* Competitive compensation
* 401(k) with employer matching
* Competitive health, dental, vision & disability coverages for employees and all dependents
* Unlimited PTO
* Paid Parental Leave Program
* Employee Referral Program