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Internship Full Stack Machine Learning Engineer Jobs in Massachusetts

Lead Machine Learning Engineer

Cambridge, MA · On-site +1

$112K - $147K/yr

... Internship experience does not apply) * At least 4 years of experience programming with Python ... Eligibility varies based on full or part-time status, exempt or non-exempt status, and management ...

Machine Learning Engineer

Somerville, MA · On-site

$170K - $200K/yr

We're looking for a Senior Machine Learning Engineer to help advance the state of voice ... Full health, dental, and vision coverage * Flexible PTO with a strong culture of taking it * Weekly ...

Machine Learning Engineer

Somerville, MA · On-site +1

$170K - $200K/yr

We're looking for a Senior Machine Learning Engineer to help advance the state of voice ... Full health, dental, and vision coverage * Flexible PTO with a strong culture of taking it * Weekly ...

Machine Learning Engineer

Somerville, MA · On-site +1

$170K - $200K/yr

We're looking for a Senior Machine Learning Engineer to help advance the state of voice ... Full health, dental, and vision coverage * Flexible PTO with a strong culture of taking it * Weekly ...

Sr. Lead Machine Learning Engineer

Cambridge, MA · On-site +1

$112K - $147K/yr

... Internship experience does not apply) * At least 4 years of experience programming with Python ... Eligibility varies based on full or part-time status, exempt or non-exempt status, and management ...

Full-time, onsite opportunity for a Senior or Staff Full Stack Engineer based in Central Square ... and machine learning teams to deliver customer-facing features • Debug and resolve complex ...

Machine Learning Engineer, Data Mining

Boston, MA · On-site +1

$124K - $149K/yr

Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail ... A solid grasp of the full ML lifecycle, from data cleaning and feature engineering to validation ...

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Design and implement complex data engineering processes to support innovative data science modeling

... full lifecycle. If you care about impact and want to help define the future of applied AI in health ... Background in multimodal or stacked models, especially combining CV outputs with tabular data.

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Design and implement complex data engineering processes to support innovative data science modeling

... Machine Learning Engineer with advanced expertise to lead development of large language models ... Proficiency with the Python deep learning software stack, particularly expertise in PyTorch, Numpy ...

... Machine Learning Engineer with advanced expertise to lead development of large language models ... Proficiency with the Python deep learning software stack, particularly expertise in PyTorch, Numpy ...

Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail ... A solid grasp of the full ML lifecycle, from data cleaning and feature engineering to validation ...

Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail ... A solid grasp of the full ML lifecycle, from data cleaning and feature engineering to validation ...

As a ML engineer with the Alexa AI team, you will be responsible for machine learning platform ... BASIC QUALIFICATIONS - 3+ years of non-internship professional software development experience - 2+ ...

Machine Learning Engineer - Computer Vision & Robotics Tycho.AI is redefining the future of autonomous intelligence. Spun out of MIT and backed by DoD contracts, we are building breakthrough AI and ...

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Showing results 1-20

Internship Full Stack Machine Learning Engineer information

What are the key skills and qualifications needed to thrive as an Internship Full Stack Machine Learning Engineer, and why are they important?

To succeed as an Internship Full Stack Machine Learning Engineer, you need a solid understanding of programming (Python, JavaScript), basic machine learning concepts, and foundational knowledge in computer science or a related field. Familiarity with frameworks like TensorFlow or PyTorch, web development tools (React, Node.js), and version control systems like Git is typically expected. Strong problem-solving abilities, collaboration skills, and a willingness to learn set exceptional interns apart. These skills enable interns to contribute effectively to both model development and deployment, bridging the gap between data science and software engineering in real-world applications.

What is an Internship Full Stack Machine Learning Engineer?

An Internship Full Stack Machine Learning Engineer is a student or early-career professional who supports both the development of machine learning models and the integration of these models into full-stack applications. This role typically involves working on data preprocessing, building and training machine learning algorithms, and deploying these models within web or mobile applications. Interns in this field gain experience in both backend and frontend technologies, as well as in machine learning frameworks and tools. The position is ideal for those seeking hands-on experience in applying AI solutions within real-world products.

What types of projects and responsibilities can I expect as an Internship Full Stack Machine Learning Engineer?

As an Internship Full Stack Machine Learning Engineer, you can expect to work on end-to-end machine learning projects that involve both model development and integration into web or cloud applications. This may include tasks like cleaning and preparing datasets, building and testing machine learning models, developing APIs to serve predictions, and collaborating with front-end developers to deliver user-facing features. Interns often work closely with data scientists, software engineers, and product managers, gaining exposure to the full development lifecycle. These experiences help build both technical and teamwork skills, laying a strong foundation for a future career in the field.

What is the difference between Internship Full Stack Machine Learning Engineer vs Software Developer Intern?

AspectInternship Full Stack Machine Learning EngineerSoftware Developer Intern
Required SkillsKnowledge of machine learning, programming (Python, JavaScript), full stack development, data handlingProficiency in programming languages (Java, Python, JavaScript), software development, basic algorithms
Work EnvironmentCollaborates on ML models, data pipelines, backend and frontend developmentFocuses on application development, coding, debugging, and testing
Industry UsageUsed in AI-driven companies, tech startups, data science teamsCommon in software firms, app development companies, tech startups

The Internship Full Stack Machine Learning Engineer role emphasizes working with machine learning models and data-driven applications, combining full stack development skills with AI expertise. In contrast, a Software Developer Intern focuses more on traditional software development tasks like coding and debugging. Both roles are valuable entry points in tech, but they target different skill sets and project types.

What are the most commonly searched types of Full Stack Machine Learning Engineer jobs in Massachusetts? The most popular types of Full Stack Machine Learning Engineer jobs in Massachusetts are:
What cities in Massachusetts are hiring for Internship Full Stack Machine Learning Engineer jobs? Cities in Massachusetts with the most Internship Full Stack Machine Learning Engineer job openings:
Lead Machine Learning Engineer

Lead Machine Learning Engineer

Capital One

Cambridge, MA • On-site, Remote

$112K - $147K/yr

Full-time

Re-posted yesterday


Capital One rating

7.8

Company rating: 7.8 out of 10

Based on 143 frontline employees who took The Breakroom Quiz

72nd of 146 rated banks


Job description

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:

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 (including AWS and Kubernetes).

  • 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, Go, Scala, or Java

Basic Qualifications:

  • Bachelor's Degree

  • At least 6 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 2 years of experience building, scaling, and optimizing ML systems

Preferred Qualifications:

  • Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field

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

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

  • 2+ years of experience developing performant, resilient, and maintainable code using Python or Go

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

  • 2+ years of people leader experience

  • 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation

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

  • Experience designing, implementing, and scaling, infrastructure using Kubernetes.

  • Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance

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

  • Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion

At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer).

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: $197,300 - $225,100 for Lead Machine Learning Engineer


McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer


New York, NY: $215,200 - $245,600 for Lead Machine Learning Engineer


San Francisco, CA: $215,200 - $245,600 for Lead Machine Learning Engineer


San Jose, CA: $215,200 - $245,600 for 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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