1

Data Annotation Engineer Jobs in Arizona (NOW HIRING)

... data analysis, including quality control, alignment, variant calling, annotation, filtering ... Collaborate with software engineering and IT teams to support cloud-based infrastructure, workflow ...

Manager - Bioinformatics

Phoenix, AZ · On-site

$150 - $210/hr

... data analysis, including quality control, alignment, variant calling, annotation, filtering ... Collaborate with software engineering and IT teams to support cloud-based infrastructure, workflow ...

... engineer. Work is completed through use of CAD software, interpreting constraints and providing back annotation. Further duties include project documentation, packaging verification and data ...

... engineer. Work is completed through use of CAD software, interpreting constraints and providing back annotation. Further duties include project documentation, packaging verification and data ...

Our customers ship better AI, faster, because we partner with their researchers from real-world data creation to annotation to delivery. We design and create datasets from scratch, recruit and manage ...

Showing results 21-40

Data Annotation Engineer information

See Arizona salary details

$48K

$137.4K

$183.6K

How much do data annotation engineer jobs pay per year?

As of Aug 21, 2026, the average yearly pay for data annotation engineer in Arizona is $137,417.00, according to ZipRecruiter salary data. Most workers in this role earn between $78,300.00 and $182,600.00 per year, depending on experience, location, and employer.

What is a data annotation engineer?

A Data Annotation Engineer is responsible for labeling and annotating data—such as text, images, audio, or video—to train machine learning models. They ensure that data is accurately categorized and structured to improve model performance. This role often involves using specialized annotation tools, following detailed guidelines, and working closely with data scientists and AI teams. Data Annotation Engineers play a crucial role in the development of AI applications by providing high-quality labeled datasets for supervised learning.

What are the key skills and qualifications needed to thrive as a data annotation engineer?

To thrive as a Data Annotation Engineer, you need a strong background in data analysis, attention to detail, and familiarity with annotation processes, often supported by a degree in computer science or a related field. Proficiency with annotation tools like Labelbox, CVAT, or VIA, and understanding of data formats used in machine learning, is commonly required. Excellent communication, collaboration, and organizational skills help you effectively manage projects and cooperate with cross-functional teams. These abilities are crucial for delivering high-quality labeled data, which directly impacts the performance of AI and machine learning models.

What are the main challenges faced by data annotation engineers in their daily work?

One of the main challenges Data Annotation Engineers face is ensuring consistent accuracy and quality in labeling large and often complex datasets. Attention to detail is critical, as even small errors can significantly affect machine learning model performance. Additionally, engineers must frequently adapt to evolving annotation guidelines and emerging data types, which requires ongoing learning and flexibility. Collaboration with data scientists and project managers is common to clarify requirements and resolve ambiguities, making strong communication skills essential for success.

What is the salary of data annotation engineer?

The salary of a data annotation engineer typically ranges from $40,000 to $80,000 annually, depending on experience, location, and the complexity of annotation tasks. Entry-level positions may start lower, while experienced professionals with specialized skills in tools like Labelbox or CVAT can earn higher salaries.

What are popular job titles related to Data Annotation Engineer jobs in Arizona?

For Data Annotation Engineer jobs in Arizona, the most frequently searched job titles are:

What job categories do people searching Data Annotation Engineer jobs in Arizona look for?

The top searched job categories for Data Annotation Engineer jobs in Arizona are:

What cities in Arizona are hiring for Data Annotation Engineer jobs?

Cities in Arizona with the most Data Annotation Engineer job openings:

Infographic showing various Data Annotation Engineer job openings in Arizona as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 10% Part Time, 2% Temporary, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $137,417 per year, or $66.1 per hour.

Staff Design Verification Engineer (AI/ML)

Analogdevices

Chandler, AZ • On-site

$133K - $163K/yr

Full-time

Re-posted 9 days ago


Job description

About Analog Devices

Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, AI, and software technologies into solutions that combat climate change, reliably connect humans and the world, and help drive advancements in automation and robotics, mobility, healthcare, energy and data centers. With revenue of more than $11 billion in FY25, ADI ensures today's innovators stay Ahead of What's Possible. Learn more at www.analog.comand on LinkedIn and X.

About the Role

As a Staff Design Verification Engineer, you will develop and execute verification strategies for complex analog and mixed-signal ICs, from test planning through coverage closure. You will apply AI/ML techniques to improve regression efficiency, debug throughput, and coverage analysis. Your work will contribute to product quality and delivery across multiple business units, ensuring ADI's next-generation mixed-signal and power management products meet rigorous quality standards.

Key Responsibilities
  • Apply AI/ML methodologies for failure clustering, regression triage, anomaly detection, and coverage optimization
  • Develop and execute verification plans, defining coverage models and success metrics for mixed-signal IC blocks and subsystems
  • Build and maintain SystemVerilog/UVM testbenches, monitors, scoreboards, and automated checkers for mixed-signal verification
  • Perform functional coverage analysis and drive coverage closure toward tape-out readiness
  • Support silicon correlation by comparing simulation expectations against lab measurements and refining tests based on results
  • Participate in verification reviews, contributing technical analysis on coverage completeness and methodology improvements
  • Collaborate with design, applications, and test teams to ensure verification reflects real use-cases and operating conditions
Required Skills and Experience
  • MSEE or MSCE with 7+ years of IC verification experience, or PhD with 5+ years (BSEE/BSCE with equivalent depth considered)
  • Strong proficiency in SystemVerilog and UVM with experience building coverage-driven verification environments for mixed-signal products
  • Demonstrated ability to deliver verification through tape-out sign-off and production release
  • Solid Python scripting skills with experience applying AI/ML techniques to enhance verification productivity
  • Knowledge of formal verification, assertion-based methodology, and clock domain crossing analysis
  • Proficiency with Cadence verification tools: Xcelium (simulation), JasperGold (formal verification)
  • Excellent presentation, technical writing, and communication skills
Preferred Qualifications
  • Curiosity and initiative to explore AI/ML techniques and emerging tools that can transform verification workflows, with an enthusiasm for discovering more effective ways of working
  • Experience with mixed-signal and AMS verification techniques including Verilog-AMS, real-number modeling, and behavioral abstraction
  • Experience with gate-level simulation, SDF back-annotation, and post-layout verification
  • Strong analytical and problem-solving abilities
  • Ability to work effectively in a collaborative team environment
Technical Scope
  • Application of AI/ML-driven workflows for regression analytics, failure classification, and debug acceleration
  • Block-level and subsystem-level verification ownership from specification analysis through coverage closure
  • Mixed-signal verification environment development including analog behavioral modeling and digital-analog interface checking
  • Test plan authoring, coverage model implementation, and regression management
Collaboration and Impact
  • Share AI/ML-driven improvements to verification workflows and best practices with team members
  • Work with design, applications, and test teams to translate product requirements into verification coverage that reflects real operating conditions
  • Contribute to verification reviews and cross-team technical discussions

For positions requiring access to technical data, Analog Devices, Inc. may have to obtain export licensing approval from the U.S. Department of Commerce - Bureau of Industry and Security and/or the U.S. Department of State - Directorate of Defense Trade Controls. As such, applicants for this position - except US Citizens, US Permanent Residents, and protected individuals as defined by 8 U.S.C. 1324b(a)(3) - may have to go through an export licensing review process.

Analog Devices is an equal opportunity employer. We foster a culture where everyone has an opportunity to succeed regardless of their race, color, religion, age, ancestry, national origin, social or ethnic origin, sex, sexual orientation, gender, gender identity, gender expression, marital status, pregnancy, parental status, disability, medical condition, genetic information, military or veteran status, union membership, and political affiliation, or any other legally protected group.

EEO is the Law: Notice of Applicant Rights Under the Law.

Job Req Type: ExperiencedRequired Travel: NoShift Type: 1st Shift/Days