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Machine Learning Distributed System Engineer Jobs

Samprasoft

Machine Learning Engineer

Durham, NC · On-site

Sr. Machine Learning Engineer Duration: 12 -24 Months Location: Merrimack, NH/ Smithfield, RI ... Strong knowledge of developing highly scalable distributed systems using Open-source technologies

Samprasoft

Machine Learning Engineer

Smithfield, RI · On-site

Sr. Machine Learning Engineer Duration: 12 -24 Months Location: Merrimack, NH/ Smithfield, RI ... Strong knowledge of developing highly scalable distributed systems using Open-source technologies

Samprasoft

Machine Learning Engineer

Roanoke, TX · On-site

Sr. Machine Learning Engineer Duration: 12 -24 Months Location: Merrimack, NH/ Smithfield, RI ... Strong knowledge of developing highly scalable distributed systems using Open-source technologies

Twilio

Principal Machine Learning & Data Engineer

$138K - $185K/yr

Lead cross-functional engineering efforts, breaking down complex initiatives into executable ... Graduate degree focused on machine learning, distributed systems, or applied statistics.

Apple

Machine Learning Engineer

Austin, TX · On-site

We are looking for a passionate, highly motivated, and hands-on applied Machine Learning Engineer ... distributed systems.Experience building data processing pipelines and large scale machine learning ...

DocuSign

Machine Learning Engineer

San Francisco, CA · On-site +1

$164K - $266K/yr

What you'll do As a Machine Learning Engineer on the AI Platform team, you will design and build ... Build and maintain high-performance distributed systems to support large-scale model inference and ...

DocuSign

Machine Learning Engineer

Seattle, WA · On-site +1

$164K - $266K/yr

What you'll do As a Machine Learning Engineer on the AI Platform team, you will design and build ... Build and maintain high-performance distributed systems to support large-scale model inference and ...

Docusign

Machine Learning Engineer

San Francisco, CA · On-site

$164K - $266K/yr

What you'll do As a Machine Learning Engineer on the AI Platform team, you will design and build ... Build and maintain high-performance distributed systems to support large-scale model inference and ...

DocuSign

Machine Learning Engineer

Seattle, WA · On-site +1

$164K - $266K/yr

What you'll do As a Machine Learning Engineer on the AI Platform team, you will design and build ... Build and maintain high-performance distributed systems to support large-scale model inference and ...

Apple

Sr. Machine Learning Engineer

Santa Clara, CA · On-site

$143K - $189K/yr

Our team comprises a diverse range of backgrounds, including applied machine learning engineers with a focus on ML and LLM, and experienced distributed systems engineers. As such, we are seeking ...

Apple

Machine Learning Engineer

Cupertino, CA · On-site

$212K - $318K/yr

Proficiency in one or more object-oriented programming languages such as Python, Java, or C++, with hands-on experience building distributed systems. Experience building large-scale machine learning ...

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Machine Learning Distributed System Engineer information

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$31.5K

$128.8K

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How much do machine learning distributed system engineer jobs pay per year?

As of Jun 4, 2026, the average yearly pay for machine learning distributed system engineer in the United States is $128,769.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $155,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Machine Learning Distributed System Engineer, and why are they important?

To thrive as a Machine Learning Distributed System Engineer, you need a strong background in computer science, distributed systems, and machine learning, often supported by a relevant degree and experience in scalable system design. Proficiency with tools like TensorFlow, PyTorch, Apache Spark, and distributed computing platforms such as Kubernetes or Hadoop is essential, along with experience in programming languages like Python, Java, or Scala. Strong problem-solving, collaboration, and communication skills help you effectively design solutions and work with cross-functional teams. These skills are crucial for building robust, scalable ML systems that can handle large datasets and support real-world AI applications.

How does a Machine Learning Distributed System Engineer typically collaborate with data scientists and software engineers on large-scale projects?

As a Machine Learning Distributed System Engineer, you will frequently work alongside data scientists to help scale their models and algorithms for production deployment. Your role involves translating prototype models into distributed systems that can handle vast datasets efficiently. You'll also coordinate with software engineers to integrate these systems into the company's technology stack, ensuring reliability and scalability. Effective communication and a collaborative mindset are crucial, as you'll help bridge the gap between research and production environments.

What is a Machine Learning Distributed System Engineer?

A Machine Learning Distributed System Engineer is a professional who designs, builds, and maintains large-scale systems that enable machine learning algorithms to process and analyze data across multiple machines or clusters. Their role often involves optimizing data pipelines, ensuring system scalability and reliability, and integrating various components to support machine learning workflows. They work closely with data scientists and software engineers to make sure that ML models can be trained and deployed efficiently on distributed infrastructure.

What is the difference between Machine Learning Distributed System Engineer vs Data Engineer?

AspectMachine Learning Distributed System EngineerData Engineer
Required CredentialsBachelor's/Master's in CS, experience with distributed systems, ML frameworksBachelor's/Master's in CS, experience with data pipelines, database systems
Work EnvironmentDeveloping scalable ML systems, working with distributed computing frameworksBuilding and maintaining data pipelines, ETL processes
Employer & Industry UsageTech companies, AI startups, research labsFinance, healthcare, e-commerce, tech firms
Search & Comparison IntentFocus on ML system scalability and distributed computingFocus on data infrastructure and pipeline management

The Machine Learning Distributed System Engineer specializes in designing and implementing scalable ML systems using distributed computing frameworks, while the Data Engineer focuses on building and maintaining data pipelines and infrastructure. Both roles require strong technical skills and often overlap in data handling, but their core focus areas differ—ML system development versus data infrastructure management.

Machine Learning Engineer

Machine Learning Engineer

Infinite Resource Solutions

Atlanta, GA • On-site

Other

Posted 23 days ago

Job description

Job Description Machine Learning Engineer Roles and Responsibilities Lead the end-to-end architecture and development of machine learning solutions. Implement machine learning algorithms into services and pipelines to be consumed at large-scale. Engineer large scale development systems using full-stack, distributed shallow and deep-learning technologies and big data technologies.

Architect and develop a highly scalable, distributed, multi-tenant set of microservices backend solutions. Be a part of a highly productive and creative engineering team What Are We Looking For in This Role. Highly Preferred: MS or PhD in Machine learning, Computer Vision, Natural Language Processing or a related field.

5+ years of experience architecting and developing AI and machine learning applications Ability to think critically, question assumptions and devise solutions to challenging technical problems. Hands-on experience with one or more of the following technologies: --Machine Learning: TensorFlow, PyTorch, Spark ML/MLib etc. --ML Technologies: NLP, Computer Vision and related technologies.

--Back end web-services: Java, Spring Boot, Python, Kubernetes, Docker - Big Data technologies: Kafka, Apache Spark, MapR, Hbase, Hive, HDFS etc. Minimum Qualifications Bachelor's Degree Relevant Experience or Degree in: Computer Science, Management Information Systems, Business or related field Typically Minimum 6 Years Relevant Exp Four-year college degree and 6 or more years, and/or a high school diploma with 8 or more years professional experience with full life cycle design and development

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