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Machine Learning Data Engineer Jobs (NOW HIRING)

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Machine Learning / Data Scientist

Bodega Bay, CA · On-site

$110K - $140K/yr

Machine Learning Engineer Location: San Francisco, CA Sponsorship: No Relocation: No Industry ... The ideal candidate is adept at using large data sets to find opportunities for product and process ...

Trimble

Machine Learning Data Scientist

Lake Oswego, OR · On-site

$122K - $165K/yr

Drive Innovation as our Next Data Engineer / Scientist (Predictive Analytics)! Ready to make a ... In this role, you will be at the forefront of developing predictive machine learning models ...

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

As of Jun 5, 2026, the average yearly pay for machine learning data engineer in the United States is $129,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,500.00 and $137,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Machine Learning Data Engineer position, and why are they important?

To thrive as a Machine Learning Data Engineer, you typically need strong programming skills in Python or Scala, a deep understanding of data structures, algorithms, and machine learning concepts, as well as a degree in computer science or a related field. Experience with big data tools like Spark, Hadoop, and cloud platforms such as AWS or Azure, along with knowledge of data pipelines and ETL processes, is highly valuable; certifications in these areas can be advantageous. Problem-solving ability, attention to detail, and strong communication skills help professionals excel when working with diverse technical teams and stakeholders. These skills ensure data engineers can effectively build reliable, scalable data systems that support the development and deployment of machine learning models.

What is a Machine Learning Data Engineer job?

A Machine Learning Data Engineer is responsible for designing, building, and maintaining the data infrastructure that supports machine learning models. They develop data pipelines, ensure data quality, and optimize data storage for efficient processing. This role involves working with large-scale datasets, implementing ETL processes, and collaborating with data scientists to deploy machine learning models. Strong knowledge of databases, cloud platforms, and programming languages like Python and SQL is essential. Their work enables organizations to leverage machine learning effectively by providing reliable and scalable data solutions.

What engineers make $300,000 a year?

Senior machine learning data engineers with extensive experience, advanced skills in data pipelines, cloud platforms, and programming languages like Python or Scala can earn $300,000 or more annually. High compensation often reflects leadership roles, specialized expertise, or work at large tech companies and financial institutions.

What are the typical daily responsibilities of a Machine Learning Data Engineer?

As a Machine Learning Data Engineer, your daily responsibilities often include designing, building, and maintaining data pipelines that efficiently move and transform data for machine learning applications. You may clean, preprocess, and validate large datasets, optimize storage solutions, and work closely with data scientists to ensure data is accessible and usable for model training and evaluation. Regular collaboration with software engineers and business analysts is common to align project goals and solve data-related challenges. Staying up to date with the latest tools and technologies is also important, as you'll help enable scalable and efficient deployment of machine learning solutions.

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Machine Learning Data Engineer jobs near you

Machine Learning Engineer

Pivotal Solutions

Manhattan, NY • On-site

Full-time

Medical, Dental, Vision

Posted 20 days ago

Job description

Job Description
Job Overview:
We are seeking a skilled Machine Learning Engineer to join our team. The ideal candidate will be responsible for designing, developing, and deploying machine learning models to solve real-world problems. You will work closely with data scientists, software engineers, and business stakeholders to implement advanced machine learning solutions and drive innovation within the company.
Key Responsibilities:
  • Design and develop scalable machine learning models and algorithms.
  • Collaborate with cross-functional teams to integrate machine learning models into production systems.
  • Analyze large datasets to extract actionable insights and identify patterns.
  • Tune and optimize machine learning models for performance and accuracy.
  • Stay current with the latest advancements in AI and machine learning technologies.
  • Work with software development teams to ensure models are deployed efficiently and effectively.
  • Develop and maintain documentation for models, algorithms, and tools used.

Requirements
  • Bachelor's or Master's degree in Computer Science, Mathematics, or related field.
  • Proven experience in machine learning, data science, and AI technologies.
  • Proficiency in Python, R, or other programming languages used in machine learning.
  • Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Strong understanding of data structures, algorithms, and statistical modeling.
  • Familiarity with cloud platforms (AWS, GCP, Azure) for deploying machine learning models.
  • Excellent problem-solving skills and the ability to work independently or in a team.
  • Strong communication skills to explain technical concepts to non-technical stakeholders.

Preferred:
  • Experience with deep learning techniques and natural language processing (NLP).
  • Prior experience in deploying machine learning models in a production environment.
  • Familiarity with DevOps practices and tools for machine learning pipelines (e.g., Docker, Kubernetes).

Benefits:
  • Competitive salary and performance bonuses.
  • Health, dental, and vision insurance.
  • Flexible working hours and remote work options.
  • Professional development opportunities.

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