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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 ...

Harvard University

Machine Learning Engineer

Boston, MA · On-site

$60/hr

Transform machine learning models into APIs to interact with other applications. * Use expert ... operational needs. When not working at a Harvard or Harvard-designated location, employees in ...

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Machine Learning Operations information

Is ML a high paying job?

Machine Learning Operations (MLOps) roles are generally well-paid due to the specialized skills required, such as expertise in cloud platforms, programming, and data management. Salaries tend to be higher than average tech roles and can increase with experience, certifications, and knowledge of tools like TensorFlow or Kubernetes.

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.

What engineer makes $500,000 a year?

Senior machine learning operations engineers with extensive experience, advanced skills in automation, cloud platforms, and deployment pipelines can earn salaries approaching or exceeding $500,000 annually, especially in high-cost-of-living areas or large tech companies. Such roles often require expertise in tools like Kubernetes, Docker, and cloud services, along with strong problem-solving and leadership abilities.

What is a $900000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers, AI research directors, or chief AI officers, often requiring advanced skills in machine learning, deep learning, and data science. These positions usually involve leadership responsibilities, extensive experience, and may include stock options or bonuses that contribute to the total compensation. Such roles are rare and highly competitive, often found in large tech companies or innovative startups.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and maintain AI systems, and while AI automation tools can handle certain tasks, MLEs are essential for creating, tuning, and overseeing complex models. AI may automate some routine aspects, but MLEs' expertise in data engineering, model optimization, and deployment remains critical for effective AI solutions.
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
Senior Manager, Machine Learning Operations (Perception)

Senior Manager, Machine Learning Operations (Perception)

Symbotic

Wilmington, MA • On-site

Full-time

Re-posted 25 days ago

Job description

Job Summary:
Symbotic is an automation technology leader reimagining the supply chain with its AI-powered robotic and software platform. The Senior Manager, Machine Learning Operations (ML Ops) leads the development and scaling of machine learning infrastructure, overseeing data pipelines and deployment systems to ensure robust performance of robotic perception systems.
Responsibilities:
• Drives the development and scaling of ML Ops infrastructure, including data pipelines, model training and validation workflows, deployment systems, and monitoring frameworks.
• Coordinates cross-functional efforts with perception, controls, and platform teams to ensure seamless integration of ML models into production robotic systems.
• Leads end-to-end ML lifecycle processes, including dataset management, model versioning, release processes, and production validation across distributed fleets.
• Manages and mentors a team of ML and data engineers, fostering a high-performing, collaborative, and execution-focused team environment.
• Ensures high-quality, reliable delivery of ML-enabled functionality by establishing best practices in reproducibility, observability, and operational excellence.
• Drives continuous improvement of monitoring systems, including software and hardware telemetry, to support system performance, diagnostics, and maintenance operations.
Qualifications:
Required:
• Requires a Bachelor’s Degree in Computer Science, Computer Engineering, Robotics, or a related field; Master’s Degree preferred or equivalent work experience.
• Minimum of 10+ years of experience in software engineering, machine learning systems, or infrastructure development.
• Minimum of 3+ years of experience managing or leading engineering teams in ML, data, or infrastructure domains.
• Proficiency in Python and/or C++; experience with distributed systems and cloud platforms (e.g., GCP), containerization (Kubernetes), and data/streaming technologies (Kafka, Snowflake).
• Strong cross-functional leadership, execution focus, communication skills, and the ability to drive process maturity in complex, fast-paced engineering environments.
Preferred:
• Experience building and scaling ML Ops platforms, including data pipelines, model deployment systems, and monitoring and observability tools.
• Experience with analytics and visualization tools (e.g., Tableau).
Company:
Symbotic is a provider of integrated supply network automation solutions for warehouses and distribution centers. Founded in 2005, the company is headquartered in Wilmington, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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