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Junior Machine Learning Engineer Jobs in Arizona

Machine Learning Engineer

Phoenix, AZ

$55.25 - $73.25/hr

Machine Learning Engineer Location: Phoenix, AZ (Onsite) Required Skills Machine Learning, Python, SQL, APIs, NLP, NoSQL, Spark / PySpark, CI/CD We are looking for a strong Machine Learning Engineer ...

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As an Applied Machine Learning Engineer, you will support informed decision-making around the application of machine learning and AI models in safety- and reliability-constrained systems. This role ...

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

As of May 29, 2026, the average yearly pay for junior machine learning engineer in Arizona is $66,909.00, according to ZipRecruiter salary data. Most workers in this role earn between $45,200.00 and $74,600.00 per year, depending on experience, location, and employer.

What Does a Junior Machine Learning Engineer Do?

As a junior machine learning engineer, you work in AI, performing research with algorithms and data modeling techniques. Machine learning involves using large collections of data to create systems that are capable of making predictions, and in this field, your duties and responsibilities revolve around using advanced mathematics to design applications for use in everything from stock trading to sports betting. Some machine learning efforts involve images, and this branch of the field is known as computer vision, while other techniques which focus on text are called natural language processing (NLP). Given these divisions, titles in machine learning include computer vision engineer, NLP scientist, or simply research scientist.

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

To succeed as a Junior Machine Learning Engineer, you need a solid grasp of programming (especially Python), foundational knowledge of algorithms and statistics, and a relevant degree in computer science, mathematics, or a related field. Familiarity with machine learning frameworks such as TensorFlow or PyTorch and tools like scikit-learn, as well as experience with version control systems like Git, are typically required. Strong problem-solving abilities, attention to detail, and a willingness to learn from feedback are valuable soft skills that help you adapt and grow in the field. These skills ensure you can effectively develop, test, and improve machine learning models while collaborating with more experienced engineers and contributing to team projects.

What kinds of projects and responsibilities can a Junior Machine Learning Engineer expect in their first year on the job?

As a Junior Machine Learning Engineer, you’ll typically work on tasks such as data preprocessing, building and testing simple models, and supporting more senior engineers in deploying machine learning solutions. Your responsibilities may also include cleaning datasets, implementing basic algorithms, and running experiments to evaluate model performance. You’ll often collaborate closely with data scientists, software engineers, and product teams to understand project goals and learn best practices. The role provides excellent opportunities to develop your technical skills, gain exposure to various stages of the ML pipeline, and gradually take on more complex projects as you grow.

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

AspectJunior Machine Learning EngineerData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; some experience with ML frameworksBachelor's or higher in CS, Statistics, or related; often advanced certifications
Work EnvironmentDeveloping and deploying ML models, coding, testingData analysis, statistical modeling, interpreting data insights
Employer & Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, tech, consulting
Search & Comparison IntentYesYes

While both roles involve working with data and machine learning, Junior Machine Learning Engineers focus on building and deploying models, often with coding and engineering skills. Data Scientists analyze data, create statistical models, and interpret insights. The roles overlap but differ mainly in their core responsibilities and skill emphasis.

What are the most commonly searched types of Machine Learning Engineer jobs in Arizona? The most popular types of Machine Learning Engineer jobs in Arizona are:
What are popular job titles related to Junior Machine Learning Engineer jobs in Arizona? For Junior Machine Learning Engineer jobs in Arizona, the most frequently searched job titles are:
What cities in Arizona are hiring for Junior Machine Learning Engineer jobs? Cities in Arizona with the most Junior Machine Learning Engineer job openings:
Infographic showing various Junior Machine Learning Engineer job openings in Arizona as of May 2026, with employment types broken down into 72% Full Time, 19% Part Time, 3% Temporary, 5% Contract, and 1% Nights. Highlights an 72% Physical, 5% Hybrid, and 23% Remote job distribution, with an average salary of $66,909 per year, or $32.2 per hour.

$55.25 - $73.25/hr

Other

Posted 7 days ago


Job description

Machine Learning Engineer

Location: Phoenix, AZ (Onsite)

Required Skills

Machine Learning, Python, SQL, APIs, NLP, NoSQL, Spark / PySpark, CI/CD

Job Description

We are looking for a strong Machine Learning Engineer with hands-on experience in developing, deploying, and optimizing ML models in enterprise environments. The ideal candidate should have expertise in Classical Machine Learning, NLP, Python development, and scalable data processing systems.

Required Qualifications

Bachelor s or higher degree in Data Science, Computer Science, Engineering, Information Systems, or related field

Hands-on experience building and deploying Machine Learning models including Classical ML and NLP solutions

Strong understanding of ML algorithms, frameworks, libraries, and software architecture

Advanced Python programming experience; Java knowledge is a plus

Experience integrating ML models into existing applications in both batch and real-time environments

Strong SQL skills with experience writing complex queries and optimizing data pipelines

Experience with NoSQL databases is a plus

Familiarity with Big Data technologies such as Spark, PySpark, Hive, MapReduce

Working knowledge of UNIX/Linux commands

Experience using GitHub and CI/CD pipelines

Strong analytical, problem-solving, and communication skills

Experience with AI/ML governance in regulated industries is a plus

Preferred Experience

NLP model development

Enterprise-scale ML deployments

Real-time inference/API integrations

Financial services or highly regulated industry background