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Data Engineer Ml Jobs (NOW HIRING)

Xforia, Inc.

Site Reliability Engineer

Austin, TX · On-site

$140K/yr

Site Reliability Engineer SRE - ML platform Responsibilities - * Continuous Deployment using GitHub Actions, Flux, Kustomize * Design and implement cloud solutions, build MLOps on cloud AWS * Data ...

Life.Church

Senior Data Engineer

Edmond, OK · On-site

$95K - $130K/yr

Enable ML & LLM Use Cases: Prepare and curate datasets suitable for predictive modeling ... Stay current on data engineering, ML, and LLM-related tools, patterns, and best practices. What It ...

Donato Technologies, Inc

Site Reliability Engineer

Austin, TX · On-site

$56.50 - $75/hr

Site Reliability Engineer SRE - ML platform Responsibilities - * Continuous Deployment using GitHub Actions, Flux, Kustomize * Design and implement cloud solutions, build MLOps on cloud AWS * Data ...

Reveille Technologies

Data Engineer

Sunnyvale, CA · On-site

$136K - $163K/yr

AI ML Data Engineer Location : Sunnyvale, CA (3 days work from office) Minimum 6 to 12 years of experience as data engineer in AI ML. * Snowflake and Python/Scala/Java * SQL, No SQL database, Hadoop ...

New

Life.Church

Senior Data Engineer

Edmond, OK · Remote

$95K - $130K/yr

Enable ML & LLM Use Cases: Prepare and curate datasets suitable for predictive modeling ... Stay current on data engineering, ML, and LLM-related tools, patterns, and best practices. What It ...

Mindlance

Data Engineer

Washington, DC · On-site

$129K - $155K/yr

Mindlance is a company seeking a Data Engineer to support programs involving cloud data pipelines ... data quality for AI/ML applications. Responsibilities : • Build continuous, event-driven ...

BC Forward

Data Engineer III

Menlo Park, CA · On-site

$71 - $74/hr

Data Engineer III Location: Menlo Park, CA (Onsite) Duration: Contract - 5 months Pay Range: $71/hr ... Hands-on integration of ML models into pipelines, including endpoint calls, versioning, batching ...

Bcbsa

Data Engineer (AI/ML)

Chicago, IL · On-site

$118K - $141K/yr

... Data Engineer will design, build, and optimize scalable, secure data pipelines that power analytics and product platforms. For this role specifically, the focus will be on Machine Learning (ML) and ...

Judge Group, Inc.

Sr. Data Engineer (AI/ML)

Reston, VA · On-site

$100K - $160K/yr

Position: Sr Data Engineer (AI/ML) Location: Remote Security Clearance: DHS Suitability - contract requires U.S. Citizenship Must Have Qualifications: 5+ years of experience in Data/ML engineering ...

YouVersion

Senior Data Engineer

Edmond, OK · On-site

$95K - $130K/yr

The Senior Data Engineer is responsible for shaping, implementing, and maintaining data pipelines ... Enable ML & LLM Use Cases~ Prepare and curate datasets suitable for predictive modeling ...

Apple

Senior ML Data Engineer, MLO

Cupertino, CA · On-site

$68.75 - $91/hr

Minimum Qualifications 7+ years of industry experience as a data engineer serving various ML applications (vision domain preferred) Bachelor's degree in Computer Science or related field Preferred ...

Medical Guardian

Senior Data Engineer

Philadelphia, PA · Remote

$107K - $145K/yr

Partner with data analysts, software engineers, ML engineers, and business stakeholders to translate requirements into technical designs and delivery priorities. * Apply data quality, validation, and ...

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All JobsData Engineer Ml Jobs

Data Engineer Ml information

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$46K

$165K

$243.5K

How much do data engineer ml jobs pay per year?

As of Jul 10, 2026, the average yearly pay for data engineer ml in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Data Engineer ML, and why are they important?

To thrive as a Data Engineer ML, you need strong programming skills (especially in Python or Scala), knowledge of data modeling, and a solid foundation in database technologies, typically supported by a degree in computer science or a related field. Familiarity with big data frameworks (like Spark or Hadoop), cloud platforms (AWS, GCP, or Azure), and ETL tools, as well as relevant certifications, is highly beneficial. Excellent problem-solving abilities, teamwork, and clear communication help you collaborate with data scientists and stakeholders effectively. These skills are essential for building robust data pipelines and infrastructure that enable scalable, high-quality machine learning solutions.

What does a Data Engineer ML do?

A Data Engineer ML (Machine Learning) is responsible for designing, building, and maintaining the data pipelines and infrastructure necessary for machine learning applications. They clean, process, and organize large datasets to ensure data quality and accessibility for data scientists and ML engineers. In addition, they may work on deploying machine learning models to production environments and optimizing data workflows for efficiency and scalability.

What is the difference between Data Engineer Ml vs Data Scientist?

AspectData Engineer MlData Scientist
Required CredentialsBachelor's in CS, Data Engineering certificationsBachelor's/Master's in CS, Data Science certifications
Work EnvironmentBuilding data pipelines, managing databasesAnalyzing data, creating models
Employer & Industry UsageTech companies, finance, healthcareResearch institutions, tech firms, finance

Data Engineer Ml focuses on developing and maintaining data infrastructure and pipelines, while Data Scientists analyze data and build predictive models. Both roles often collaborate but serve different functions within data teams.

How do Data Engineer ML roles typically collaborate with data scientists and machine learning engineers on projects?

Data Engineer ML professionals work closely with data scientists and machine learning engineers by building and maintaining robust data pipelines, ensuring clean and reliable datasets are readily available for modeling and analysis. They often participate in meetings to understand model requirements, help optimize data storage for performance, and support the deployment of machine learning models into production environments. Effective collaboration involves continuous communication to troubleshoot data issues, implement data validation, and scale solutions as project needs evolve. This teamwork ensures that data-driven projects move efficiently from experimentation to deployment.
More about Data Engineer Ml jobs
What cities are hiring for Data Engineer Ml jobs? Cities with the most Data Engineer Ml job openings:
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What job categories do people searching Data Engineer Ml jobs look for? The top searched job categories for Data Engineer Ml jobs are:
Data Engineer Ml jobs near you
Infographic showing various Data Engineer Ml job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $165,018 per year, or $79.3 per hour.
Site Reliability Engineer

Site Reliability Engineer

Xforia, Inc.

Austin, TX • On-site

$140K/yr

Full-time

Posted 21 days ago

Job description

Title: Site Reliability Engineer SRE - ML platform
Location: Austin, TX OR Sunnyvale, CA
Type: FTE
Salary/Rate : $140K
Title: Site Reliability Engineer SRE - ML platform
Responsibilities -
  • Continuous Deployment using GitHub Actions, Flux, Kustomize
  • Design and implement cloud solutions, build MLOps on cloud AWS
  • Data science model containerization, deployment using docker, VLLM, Kubernetes
  • Communicate with a team of data scientists, data engineers and architects, document the processes
  • Develop and deploy scalable tools and services for our clients to handle machine learning training and inference.
  • Knowledge of ML models and LLM

Qualifications:
  • 6+ years of experience in ML Ops with strong knowledge in Kubernetes, Python, MongoDB and AWS.
  • Good understanding of Apache SOLR.
  • Proficient with Linux administration.
  • Knowledge of ML models and LLM.
  • Ability to understand tools used by data scientists and experience with software development and test automation
  • Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS)
  • Experience working with cloud computing and database systems
  • Experience building custom integrations between cloud-based systems using APIs
  • Experience developing and maintaining ML systems built with open-source tools
  • Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., experience with Docker and Kubernetes
  • Experience developing containers and Kubernetes in cloud computing environments
  • Familiarity with one or more data-oriented workflow orchestration frameworks (Kubeflow, Airflow, Argo, etc.)
  • Ability to translate business needs to technical requirements
  • Strong understanding of software testing, benchmarking, and continuous integration
  • Exposure to machine learning methodology and best practices
  • Good communication skills and ability to work in a team

Note: Focus is to have 60% SRE and 40% ML Ops...

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