1

Data Engineer Ml Jobs (NOW HIRING)

Sr. Data Engineer

Houston, TX · On-site

$101K - $137K/yr

ML engineers own the features and models built on top of it. The training and monitoring layer is shared ground you design together. Qualifications : Required : • 4+ years in data engineering or a ...

Data Engineer

Manhattan, NY · On-site

$126K - $151K/yr

Job Title- Data Engineer Location- New York, NY 10112 Reporting Type- Onsite Duration: 10 months Summary This role involves building and delivering advanced data science and AI/ML solutions in an ...

Erwartungsmanagement Anforderungen Mehrjahrige Erfahrung als MLOps Engineer, ML Engineer oder Data ... Engineer Sehr gute Kenntnisse in Kubernetes-/OpenShift-basierten Umgebungen Erfahrung mit ML ...

Sr. Data Engineer

Ann Arbor, MI · On-site

$103K - $140K/yr

ML engineers own the features and models built on top of it. The training and monitoring layer is shared ground you design together. Qualifications : Required : • 4+ years in data engineering or a ...

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 ...

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 ...

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 ...

Google Senior Data Engineer

Albany, NY · On-site

$113K - $136K/yr

Implement ML pipelines and help establish MLOps processes (monitoring, retraining, deployment ... Collaborate closely with senior data engineers, ML engineers, and architects. * Contribute to ...

Senior Data Engineer

Edmond, OK · On-site

$95K - $130K/yr

... with ML-focused engineers. • Contribute to data governance, stewardship, privacy, and security best practices. • Build testing, monitoring, and alerting to ensure high data quality and early ...

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 ...

Staff ML Data Engineer (Datagrid)

San Francisco, CA · On-site

$134K - $162K/yr

We're looking for a Staff ML Data Engineer to join Procore's AI & Frontier Models organization. In this role, you'll be responsible for designing and building the data systems that power frontier ...

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 ...

Staff ML Data Engineer (Datagrid)

San Francisco, CA · On-site

$134K - $162K/yr

We're looking for a Staff ML Data Engineer to join Procore's AI & Frontier Models organization. In this role, you'll be responsible for designing and building the data systems that power ...

Showing results 41-60

Data Engineer Ml information

See salary details

$46K

$165K

$243.5K

How much do data engineer ml jobs pay per year?

As of Aug 19, 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 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 are the key skills and qualifications needed to thrive as a data engineer ML?

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.

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.

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.

More about Data Engineer Ml jobs

What cities are hiring for Data Engineer Ml jobs?

Cities with the most Data Engineer Ml job openings:

What states have the most Data Engineer Ml jobs?

States with the most job openings for Data Engineer Ml jobs include:

Infographic showing various Data Engineer Ml job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $165,018 per year, or $79.3 per hour.

AI/ML Data Integration Architect / Data Engineer (AI/ML)

Saransh Inc

Minneapolis, MN • On-site

Contractor

Re-posted yesterday


Job description

Role: Data Integration Architect / Data Engineer (AI/ML)
Location: Minneapolis, MN (Hybrid)
Job Type: Contract
 
Required 10+ years of experience
 
Position Summary:
  • We are looking for a highly skilled AWS + Databricks professional with a strong background in Artificial Intelligence / Machine Learning (AI/ML) and cloud-based data solutions
  • The ideal candidate will serve as a Solution Architect, leading technical designs and implementations across data pipelines, ML workflows, and scalable analytics platforms.
 
Responsibilities: 
  • Design and implement end-to-end ML solutions using AWS and Databricks
  • Architect and optimize data lakes / lakehouses using Delta Lake, S3, Glue, and Spark
  • Manage feature engineering, model training, evaluation, and deployment workflows via MLflow or SageMaker
  • Lead the architecture of scalable AI/ML pipelines
  • Collaborate with Data Engineers, MLOps teams, and business stakeholders to translate ML use cases into production systems
  • Enforce best practices for model governance, data security, and CI/CD in ML environments