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Associate Machine Learning information

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$22K

$115.8K

$283.9K

How much do associate machine learning jobs pay per year?

As of Aug 30, 2026, the average yearly pay for associate machine learning in Texas is $115,791.00, according to ZipRecruiter salary data. Most workers in this role earn between $52,300.00 and $160,500.00 per year, depending on experience, location, and employer.

What does an associate machine learning engineer do?

An Associate Machine Learning Engineer assists in designing, developing, and deploying machine learning models under the supervision of senior engineers. They handle tasks such as data preprocessing, model evaluation, and maintaining machine learning pipelines. Associates often collaborate with data scientists, software engineers, and business teams to ensure that machine learning solutions are integrated effectively into products or services. This role is typically entry-level or early career and is a stepping stone toward more advanced machine learning positions.

What are the key skills and qualifications needed to thrive as an associate machine learning engineer?

To thrive as an Associate Machine Learning Engineer, you need a solid background in mathematics, programming (especially Python), and foundational machine learning concepts, usually supported by a relevant degree. Familiarity with tools like TensorFlow, PyTorch, scikit-learn, and experience with data processing libraries and version control systems is typically required. Strong analytical thinking, problem-solving ability, and effective collaboration skills help you stand out in this role. These competencies are essential for developing robust models, working efficiently with teams, and delivering impactful data-driven solutions.

What are some common challenges faced by associate machine learning professionals when transitioning from academic projects to real-world business applications?

Associate Machine Learning professionals often find that moving from academic or theoretical projects to business-focused environments introduces new challenges. Real-world datasets can be messy, incomplete, or imbalanced, requiring additional data cleaning and preprocessing. Moreover, business timelines may require rapid prototyping and iterative model development, which is different from the more open-ended nature of academic research. Collaborating with cross-functional teams such as data engineers, product managers, and business stakeholders is also essential to align models with organizational goals. Adapting to these practical aspects is key to succeeding in an Associate Machine Learning role.

What is the difference between Associate Machine Learning vs Data Scientist?

AspectAssociate Machine LearningData Scientist
Required CredentialsBachelor's degree in CS, Data Science, or related field; some roles may require certifications in ML or AIBachelor's or Master's in CS, Statistics, or related; often requires experience with data analysis and programming
Work EnvironmentEntry-level, team-based projects, focused on supporting ML models and data preprocessingMore autonomous, involved in data analysis, model development, and interpretation
Employer & Industry UsageTech companies, startups, research labs; roles in AI and ML teamsWide range of industries including tech, finance, healthcare, and consulting

While both roles involve working with data and machine learning, an Associate Machine Learning typically focuses on supporting ML projects with less experience, whereas a Data Scientist has broader responsibilities including data analysis, model development, and strategic insights. The roles often overlap but differ in scope and experience level.

What are the most commonly searched types of Machine Learning jobs in Texas?

The most popular types of Machine Learning jobs in Texas are:

What cities in Texas are hiring for Associate Machine Learning jobs?

Cities in Texas with the most Associate Machine Learning job openings:

Associate Machine Learning jobs near you

Machine Learning Developer

Dallas, TX

Diamondbackenergy
See more jobs at Diamondbackenergyexternal

Full-time

Posted 3 days ago

New

Job description

CURRENT EMPLOYEES - Please apply using "Jobs Hub" in Workday. This career site is for external applicants only.

The Machine Learning (ML) Developer is the first dedicated ML Development role in the department and is responsible for establishing the development practices, standards, and platform foundations that move machine learning models from experimentation into reliable, governed production. Working primarily within the Databricks ecosystem, the ML Developer will define how models are built, tracked, deployed, and monitored, and will coordinate with data science teams and technical professionals across the organization to ensure company objectives and goals are met.


Job Responsibilities:

Include but are not limited to

  • Establish the department's MLOps standards, reusable pipeline patterns, and "golden path" for taking a model from notebook to production
  • Partner with data science teams to productionize models using Databricks MLflow, AutoML, Unity Catalog, and Model Serving
  • Design and maintain automated CI/CD pipelines for model training, deployment, and controlled promotion across environments
  • Govern the model lifecycle through experiment tracking, model registration, versioning, lineage, and access control
  • Establish model and data monitoring, validation checks, and operational observability; support incident response and reliability of production ML systems
  • Enforce data and feature quality, schema validation, and data versioning so models train and infer on trusted inputs
  • Author documentation, reference architectures, and playbooks; lead code reviews and knowledge-sharing to drive consistent engineering practice
  • Coordinate with business stakeholders, data scientists, data engineers, and IT to define requirements and drive adoption of shared frameworks
  • Evaluate emerging tools and patterns, including agentic and LLM-assisted development workflows, and recommend improvements to ML delivery

Required Qualifications:

  • Bachelor's Degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related field
  • Must have hands-on experience with Databricks MLflow and AutoML
  • Three (3) to five (5) years of hands-on experience building, deploying, and operating machine learning or data-intensive systems in production
  • Strong proficiency in Python as a primary engineering language, with experience writing tested, maintainable production code
  • Strong SQL skills and working knowledge of Spark or other distributed data processing frameworks
  • Practical experience establishing or operating an MLOps workflow, including model deployment, pipeline automation, monitoring, and lifecycle management
  • Software engineering fundamentals including version control (Git), unit testing, CI/CD, and common design patterns
  • Ability to explain the intuition behind common ML algorithms and follow model training, evaluation, and hyperparameter tuning best practices
  • Strong interpersonal, analytical, and communication skills, with the ability to work effectively across data science, engineering, and business teams

Preferred Qualifications:

  • Experience with Unity Catalog for model governance, lineage, and controlled promotion of ML assets
  • Databricks certification (e.g., Databricks Certified Machine Learning Associate or Professional)
  • Master's Degree in a related field
  • Familiarity with cloud data platforms, infrastructure-as-code, containerization and orchestration
  • Exposure to LLM/GenAI application patterns such as RAG and evaluation harnesses, and to agentic or AI-assisted development workflows
  • Experience mentoring or training data scientists on engineering best practices
  • Ability to operate both independently and as part of a team
  • Self-starter requiring minimal supervision with strong organizational and time management skills

Diamondback is an Equal Employment Opportunity Employer. Diamondback provides equal employment opportunities to all qualified applicants without regard to race, sex, sexual orientation, gender identity, national origin, color, age, religion, veteran or disability status, genetic information, pregnancy, or any other status protected by law. Diamondback participates in E-Verify. Learn more aboutE-Verify.

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