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Machine Learning Developer

Pay
Pay not listedSimilar roles state $166K–$240K
Work setting
Not stated
Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting

What you’ll work on

Full posting
  • Set MLOps standards and reusable pipeline patterns for taking models from notebook to production

  • Productionize models with Databricks MLflow, AutoML, Unity Catalog and Model Serving

  • Design and maintain CI/CD pipelines for model training, deployment and promotion

  • Govern model lifecycle: tracking, registration, versioning, lineage and access control

  • Set up model and data monitoring, validation checks and incident response for production ML

From the employer’s posting
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

What you’ll bring

All qualifications

Core experience

  • Bachelor’s Degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related field
  • 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
  • Ability to explain the intuition behind common ML algorithms and follow model training, evaluation, and hyperparameter tuning best practices

Preferred experience

  • Experience with Unity Catalog for model governance, lineage, and controlled promotion of ML assets
  • Master’s Degree in a related field
  • Familiarity with cloud data platforms, infrastructure-as-code, containerization and orchestration
  • Experience mentoring or training data scientists on engineering best practices
Qualification wording
Bachelor’s Degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related field
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
Ability to explain the intuition behind common ML algorithms and follow model training, evaluation, and hyperparameter tuning best practices
Experience with Unity Catalog for model governance, lineage, and controlled promotion of ML assets
Master’s Degree in a related field
Familiarity with cloud data platforms, infrastructure-as-code, containerization and orchestration
Experience mentoring or training data scientists on engineering best practices
Education & alternatives
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
- 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
Apply at Diamondback

Posting record

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Status in our records
Live
First seen by us
Aug 26, 202670 days ago
Recorded sightings
61
Last seen by us
Nov 5, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

$166K–$240K is typical

The middle half of what similar US postings state. This one doesn’t list pay.

From 1,279 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 70

Similar jobs usually come down between day 6 and day 70.

Well past Diamondback’s usual: its jobs come down by about day 24.

From 4,442 similar postings that came down, and 5 of Diamondback’s. Coming down is not always a hire.

Diamondback is hiring

3 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Takes jobs down at a usual pace
Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • SQL
  • Databricks
  • MLflow
  • Spark
Where the posting names them
- 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
- 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
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:
- 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
- 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
- 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

Your next step

Complete your application on diamondbackenergy.wd12.myworkdayjobs.com. The employer’s form will show what is required.

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Already applied? Track this application

About Diamondback

People
About 1,762 in 2025, down 11% from the year before.Annual report (SEC Form 10-K)
Company
Listed as FANG.

Demand for ML Engineer roles

New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.

Weekly new ML Engineer postings at the same employers, from the week of August 17 to the week of October 26. The fewest in a week was 625, the most 1,094, and the latest week had 876.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 5 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

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 about E-Verify.

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Dallas, TX

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