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Google Cloud Machine Learning Engineer Jobs in Massachusetts

Machine Learning Engineer Architect, build, and operate end-to-end ML pipelines for training, validation and deployment on Google Cloud and AWS. Define, instrument, and maintain logging, monitoring ...

Machine Learning Engineer - Cloud

Lowell, MA · On-site +1

$86K - $135K/yr

Machine Learning Engineer - Cloud *Please consider before applying: This is a hybrid role, and candidates must reside within a commutable distance of one of our offices in either Dover, NH, or Lowell ...

Machine Learning Engineer

Dorchester, MA · On-site

$175K - $250K/yr

Machine Learning Engineer Chicago, United States; Hong Kong, Hong Kong; Sydney, Australia As a ... Exposure to cloud platforms and orchestration tools * A track record of contributing to open-source ...

We are seeking a Machine Learning Engineer to join our team at MORSE. You will play a pivotal role ... Experience with cloud platforms (AWS and Azure) * Experience with Docker * Experience with MLOps ...

Sr. Machine Learning Engineer Duration: 12 -24 Months Location: Merrimack, NH/ Smithfield, RI ... the Cloud using AWS Sagemaker * Extensive experience working with machine learning models with ...

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... cloud and on-premise environments * Coordinate with cross-functional teams to deploy models and ...

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... cloud and on-premise environments * Coordinate with cross-functional teams to deploy models and ...

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... cloud and on-premise environments * Coordinate with cross-functional teams to deploy models and ...

Google Cloud (AGBG) Sales Engineer

Boston, MA · On-site

$60.50 - $81/hr

Accenture Google Business Group (AGBG) focuses on Cloud solutions leveraging Google's Cloud ... To accelerate our customers transformation leveraging cloud, we combine world-class learning and ...

Google Cloud Data Engineer

Boston, MA · Hybrid

$124.40K - $149.40K/yr

Google Cloud Data Engineer Are you ready to step up to the New and take your technology expertise ... Knowledge in machine learning algorithms especially in recommender systems * Extracting, Loading ...

Develop and deploy machine learning models for optimal performance and scalability. * Productivity ... Cloud and Containerization: Utilize Kubernetes for managing Docker containers and various cloud ...

Machine Learning Engineer

Cambridge, MA · On-site

$125.10K - $150.30K/yr

Develop and deploy machine learning models for optimal performance and scalability. * Productivity ... Cloud and Containerization: Utilize Kubernetes for managing Docker containers and various cloud ...

Machine Learning Engineer - Computer Vision & Robotics Tycho.AI is redefining the future of ... Collaborate with top engineers and researchers from MIT, Google, and across the defense innovation ...

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Showing results 1-20

Google Cloud Machine Learning Engineer information

See Massachusetts salary details

$25

$68

$95

How much do google cloud machine learning engineer jobs pay per hour?

As of May 28, 2026, the average hourly pay for google cloud machine learning engineer in Massachusetts is $68.68, according to ZipRecruiter salary data. Most workers in this role earn between $58.56 and $78.22 per hour, depending on experience, location, and employer.

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

To thrive as a Google Cloud Machine Learning Engineer, you need strong programming skills in Python or Java, a deep understanding of machine learning algorithms, and a degree in computer science or a related field. Familiarity with Google Cloud Platform (GCP) services such as Vertex AI, BigQuery, TensorFlow, and relevant certifications like the Professional Machine Learning Engineer certification is highly valuable. Excellent problem-solving abilities, collaboration, and clear communication make someone stand out in this position. These skills and qualities are critical for designing, deploying, and optimizing scalable ML solutions that meet business objectives in cloud environments.

What are some typical cross-functional collaborations for a Google Cloud Machine Learning Engineer?

As a Google Cloud Machine Learning Engineer, you'll frequently work alongside data scientists, software engineers, and product managers to design, deploy, and maintain machine learning solutions at scale. Collaboration often involves translating business requirements into machine learning pipelines, integrating models into cloud-based applications, and ensuring that solutions are robust, secure, and scalable. Regular communication with DevOps and infrastructure teams is also common to optimize model deployment and monitor performance. This cross-disciplinary teamwork is crucial for delivering impactful, production-ready AI solutions.

What are Google Cloud Machine Learning Engineers?

Google Cloud Machine Learning Engineers are professionals who design, build, and deploy machine learning models using Google Cloud Platform (GCP) services and tools. They work with large datasets, develop scalable ML solutions, and collaborate with data scientists and software engineers. Their role often includes automating data pipelines, optimizing model performance, and ensuring the reliability and security of ML deployments on the cloud. These engineers have expertise in both machine learning algorithms and cloud infrastructure, making them key contributors to data-driven projects.

What is the difference between Google Cloud Machine Learning Engineer vs Data Scientist?

AspectGoogle Cloud Machine Learning EngineerData Scientist
Required CredentialsGoogle Cloud certifications, programming skills, ML knowledgeStatistics, data analysis, programming, often with advanced degrees
Work EnvironmentCloud platforms, coding, deploying ML modelsData analysis, modeling, reporting, often in research or business settings
Employer & Industry UsageTech companies, cloud service providers, enterprises using Google CloudVarious industries including finance, healthcare, marketing, research

Google Cloud Machine Learning Engineers focus on developing and deploying ML models on Google Cloud, requiring cloud certifications and coding skills. Data Scientists analyze data, build models, and generate insights, often with advanced degrees. While both roles work with data and ML, the Engineer role emphasizes cloud deployment and infrastructure, whereas Data Scientists focus on data analysis and modeling.

What are popular job titles related to Google Cloud Machine Learning Engineer jobs in Massachusetts? For Google Cloud Machine Learning Engineer jobs in Massachusetts, the most frequently searched job titles are:
What job categories do people searching Google Cloud Machine Learning Engineer jobs in Massachusetts look for? The top searched job categories for Google Cloud Machine Learning Engineer jobs in Massachusetts are:
What cities in Massachusetts are hiring for Google Cloud Machine Learning Engineer jobs? Cities in Massachusetts with the most Google Cloud Machine Learning Engineer job openings:
Senior Lead Machine Learning Engineer

Senior Lead Machine Learning Engineer

Capital One

Cambridge, MA • On-site, Remote

Full-time

Posted 16 days ago


Capital One rating

7.7

Company rating: 7.7 out of 10

Based on 134 frontline employees who took The Breakroom Quiz

74th of 141 rated banks


Job description

Senior Lead Machine Learning Engineer

As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.

What you'll do in the role:

The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:

  • Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.

  • Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation).

  • Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.

  • Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.

  • Retrain, maintain, and monitor models in production.

  • Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.

  • Construct optimized data pipelines to feed ML models.

  • Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.

  • Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.

  • Use programming languages like Python, Scala, or Java.

Basic Qualifications:

  • Bachelor's Degree

  • At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)

  • At least 4 years of experience programming with Python, Scala, or Java

  • At least 3 years of experience building, scaling, and optimizing ML systems

  • At least 2 years of experience leading teams developing ML solutions

Preferred Qualifications:

  • Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field

  • Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform

  • 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, Kubeflow or TensorFlow

  • 3+ years of experience developing performant, resilient, and maintainable code

  • 3+ years of experience with data gathering and preparation for ML models

  • ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents

  • 3+ years of experience building production-ready data pipelines that feed ML models

  • Ability to communicate complex technical concepts clearly to a variety of audiences

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.

Cambridge, MA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer


McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer


New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer


San Francisco, CA: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer


San Jose, CA: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer







Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter.

This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at theCapital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.

This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.

If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.

For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com

Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.

Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).


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