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Machine Learning Operations Jobs in Massachusetts

Ayo Semiconductor

Machine Learning Architect

Boston, MA · On-site

They have a full-stack understanding of machine learning architectures, love to optimize algorithms ... Deep knowledge of the structure and internal operation of neural networks - including how and why ...

Ayosemi

Machine Learning Architect

Boston, MA · On-site

$120 - $160/hr

They have a full‑stack understanding of machine learning architectures, love to optimize ... Deep knowledge of the structure and internal operation of neural networks - including how and why ...

C the Signs

Senior Machine Learning Engineer

Boston, MA · On-site +1

$133K - $175K/yr

Position Summary The Machine Learning Engineer will be responsible for the end-to-end development ... operational problems. Set up and manage the training environment, including GPU instances and ...

C the Signs

Senior Machine Learning Engineer

Boston, MA · On-site +1

$133K - $175K/yr

Position Summary The Machine Learning Engineer will be responsible for the end-to-end development ... operational problems. Set up and manage the training environment, including GPU instances and ...

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All JobsMachine Learning Operations JobsMachine Learning Operations Jobs in Massachusetts

Machine Learning Operations information

What is the difference between Machine Learning Operations vs Data Scientist?

AspectMachine Learning OperationsData Scientist
Primary FocusDeploying, maintaining, and scaling ML models in productionAnalyzing data to develop insights and build models
Required SkillsML deployment, cloud platforms, automation, scriptingStatistical analysis, data visualization, programming (Python/R)
Work EnvironmentOperations teams, cloud infrastructure, production systemsResearch environments, data analysis teams, R&D
Common CertificationsCloud certifications, MLOps tools certificationsData science certifications, statistical courses

Machine Learning Operations and Data Scientists often collaborate, but MLOps focuses on deploying and maintaining models in production, while Data Scientists focus on analyzing data and developing models. Both roles require technical skills, but their day-to-day tasks and environments differ.

Is machine learning operations a high paying job?

Machine Learning Operations (MLOps) roles typically offer high salaries due to the specialized skills required, such as expertise in cloud platforms, automation, and data engineering. Compensation varies based on experience, location, and company size, but generally ranks among well-paying tech jobs.

What are machine learning operations?

Machine Learning Operations (MLOps) is a set of practices that combines machine learning, software engineering, and DevOps to deploy, monitor, and maintain machine learning models in production environments. It involves tasks such as model versioning, automation, testing, and ensuring scalability and reliability using tools like CI/CD pipelines and cloud platforms.

What cities in Massachusetts are hiring for Machine Learning Operations jobs?

Cities in Massachusetts with the most Machine Learning Operations job openings:

Machine Learning Operations jobs near you
Infographic showing various Machine Learning Operations job openings in Massachusetts as of August 2026, with employment types broken down into 1% As Needed, 86% Full Time, 9% Part Time, 1% Temporary, 2% Contract, and 1% Nights. Highlights an 93% Physical, 3% Hybrid, and 4% Remote job distribution.

Senior Machine Learning Operations Engineer

CarGurus

Boston, MA

$113K - $155K/yr

Full-time

Re-posted 25 days ago

Job description

Role overview

As a core member of the Machine Learning Platform team, the Machine Learning Operations Engineer will be responsible for enhancing and maintaining CarGurus' cloud-hosted ML platform. You will partner closely with data scientists to deploy machine learning models to production and to build and maintain the APIs and data pipelines that integrate predictive intelligence into CarGurus' products. You will have the opportunity to contribute to systems supplying Recommendations, Search Ranking, Computer Vision, Instant Market Value, and more.

What you'll do

  • Write production-quality training jobs and inference APIs for our Python ML models, deploying them to robust scalable services
  • Contribute enhancements to the CarGurus ML platform, leveraging technologies such as AWS SageMaker, GitHub Actions, and Docker
  • Participate in systems design conversations with our data scientists and engineering partners, using your engineering expertise and experience to help them design scalable and robust systems
  • Develop in-house tools and libraries to standardize and accelerate the AI and ML development process
  • Build and integrate multi-modal AI capabilities into production ML systems
  • Design and build agentic AI systems and harness engineering to enable autonomous, reliable software development workflows
  • Own and maintain aspects of the Data Science team's engineering infrastructure 
  • Promote and foster an inclusive, transparent, and collaborative culture

What you'll bring

  • 4+ years experience writing and debugging Python code
  • Familiarity with software engineering tools and standard methodologies, e.g. git, unit testing, object-oriented design, containerization 
  • A working understanding of the machine learning lifecycle, including model training, evaluation, deployment, and monitoring
  • Familiarity with the Python ML ecosystem (e.g. scikit-learn, XGBoost, PyTorch, numpy, pandas) 
  • Experience deploying, monitoring, and troubleshooting AI & ML models in a public cloud (we use AWS) 
  • Exposure to a variety of AI & ML systems in production, including regression and classification on tabular data, image models, language models, and agentic systems
  • Knowledge of SQL and familiarity with cloud data warehouses (we use Snowflake)
  • A systems thinking mindset, able to reason about how components interact end-to-end across the ML lifecycle and platform, and also optimizing any single piece

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