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Ai Machine Learning Engineer Jobs in Arizona (NOW HIRING)

Bespoke Labs

Machine Learning Engineer

Chandler, AZ · On-site

About Us We are AI researchers and builders who understand how to curate data and RL environments ... Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post ...

Programmers.io

AI/ML Engineer

Scottsdale, AZ · On-site

Implement CICD pipelines for ML and AI-driven applications. Monitor, troubleshoot, and optimize ... Google Cloud Professional Machine Learning Engineer Google Cloud Professional Data Engineer AWS ...

Vaco by Highspring

Staff / Principal AI Engineer

Gilbert, AZ · On-site

$170K - $190K/yr

Lead the design, development, and deployment of scalable AI and machine learning solutions across ... Partner with product, architecture, data, engineering, innovation, and marketing teams to identify ...

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All JobsAi Machine Learning Engineer JobsAi Machine Learning Engineer Jobs in Arizona

Ai Machine Learning Engineer information

See Arizona salary details

$29.4K

$120K

$180.3K

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

As of Jul 14, 2026, the average yearly pay for ai machine learning engineer in Arizona is $119,998.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,600.00 and $144,400.00 per year, depending on experience, location, and employer.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position such as a senior AI or machine learning engineer, research director, or executive role that offers a total compensation package including salary, bonuses, and stock options. These roles usually require advanced skills in deep learning, data science, and experience with tools like TensorFlow or PyTorch, often in competitive tech or finance industries. Such compensation reflects significant expertise, leadership responsibilities, and impact on strategic AI initiatives.

What are the key skills and qualifications needed to thrive as an AI Machine Learning Engineer, and why are they important?

To thrive as an AI Machine Learning Engineer, you need a strong background in mathematics, statistics, programming (often Python or R), and a relevant degree such as computer science or engineering. Familiarity with frameworks like TensorFlow, PyTorch, and scikit-learn, as well as experience with cloud platforms and data processing tools, is highly valued, along with certifications in AI or machine learning. Critical thinking, problem-solving, and effective communication are essential soft skills for collaborating with teams and translating business needs into technical solutions. These competencies are crucial for developing accurate, scalable AI models that deliver real-world value and drive innovation.

What are some common challenges that AI Machine Learning Engineers face when deploying models to production environments?

AI Machine Learning Engineers often encounter challenges such as ensuring model scalability, managing data pipeline reliability, and handling model drift once solutions are live. They also need to collaborate closely with DevOps and software engineering teams to integrate models seamlessly into existing systems, while maintaining performance and security. Addressing these challenges requires a strong understanding of both machine learning principles and software deployment best practices.

What engineers make $500,000?

Senior AI Machine Learning Engineers with extensive experience, advanced skills in deep learning, and proficiency in tools like TensorFlow or PyTorch can earn $500,000 or more annually, especially in high-cost-of-living areas or within large tech companies. Achieving this level often requires a strong track record, specialized certifications, and leadership responsibilities.

Is AI ML engineer in demand?

AI and ML engineers are in high demand across various industries due to the increasing adoption of artificial intelligence technologies. Companies seek professionals skilled in programming, data analysis, and machine learning frameworks like TensorFlow and PyTorch to develop and deploy AI solutions, leading to strong job growth and competitive salaries in this field.

Which 3 jobs will survive AI?

AI Machine Learning Engineers are likely to continue to be in demand because they develop and refine AI models, requiring specialized skills in programming, data analysis, and domain knowledge. Jobs that involve complex problem-solving, creativity, and emotional intelligence, such as healthcare professionals, educators, and skilled trades, are also expected to persist alongside AI advancements. These roles often require human judgment and adaptability that AI cannot fully replicate.

What is an AI Machine Learning Engineer?

An AI Machine Learning Engineer is a professional who designs, builds, and deploys artificial intelligence and machine learning models to solve real-world problems. They work with large datasets, select appropriate algorithms, and optimize models for accuracy and efficiency. Their role often involves both software engineering and data science skills, and they collaborate with other teams to integrate these models into products or services. AI Machine Learning Engineers are in high demand across industries such as technology, healthcare, finance, and more.

What is the difference between Ai Machine Learning Engineer vs Data Scientist?

AspectAi Machine Learning EngineerData Scientist
CredentialsDegree in CS, AI, or related fields; certifications in ML frameworksDegree in CS, Statistics, or related fields; certifications in data analysis
Work EnvironmentDevelops and deploys ML models in production systemsAnalyzes data, builds models, and provides insights
Industry UsageTech, finance, healthcare, where deploying ML models is keyResearch, business intelligence, analytics across industries

While both roles involve working with data and machine learning, Ai Machine Learning Engineers focus on building and deploying scalable ML models in production environments, whereas Data Scientists primarily analyze data and develop models for insights. The roles often overlap but differ in their core focus and responsibilities.

What are popular job titles related to Ai Machine Learning Engineer jobs in Arizona? For Ai Machine Learning Engineer jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Ai Machine Learning Engineer jobs in Arizona look for? The top searched job categories for Ai Machine Learning Engineer jobs in Arizona are:
Ai Machine Learning Engineer jobs near you
Infographic showing various Ai Machine Learning Engineer job openings in Arizona as of July 2026, with employment types broken down into 78% Full Time, 19% Part Time, and 3% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution, with an average salary of $119,998 per year, or $57.7 per hour.

Machine Learning Engineer

Bespoke Labs

Chandler, AZ • On-site

Full-time

Posted 26 days ago

Job description

About Us

We are AI researchers and builders who understand how to curate data and RL environments that truly improve models. We curated OpenThoughts, one of the best open reasoning datasets, and have trained SOTA models such as Bespoke-MiniCheck and Bespoke-MiniChart.

We are embarked on a journey to build Environments that are entire digital worlds that can be used to push the frontier of agents.

What You'll Be Working On

You will work directly with our research team on RL environment and task creation for agent training. This means designing observation spaces, action spaces, reward signals, and success criteria for new environments — and building the infrastructure that makes world-scale RL training possible. This is a high-ownership role; you will be building novel systems, not maintaining legacy ones.

Must-Have Skills

3+ years of ML engineering experience — model training, fine-tuning, or post-training pipelines in research or production

Strong Python and deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed precision)

Hands-on experience with LLM post-training — SFT, RLHF, PPO, DPO, or reward model training — and understanding of how training data quality affects model behavior

Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or modify reward functions for agent training objectives

Experience running experiments at scale on cloud or HPC (AWS, GCP, SLURM, or Ray)

Solid understanding of evaluation methodology — held-out sets, benchmark design, avoiding train/eval contamination

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