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Internship Full Stack Machine Learning Engineer Jobs in Belmont, MA

Senior Machine Learning Engineer

Boston, MA ยท On-site +1

$133K - $175K/yr

Position Summary The Machine Learning Engineer will be responsible for the end-to-end development and deployment of Large language and machine learning models, with a primary focus on data ...

Be it core Java, full-stack Java, Web/UI designers, Big Data or Cloud or Mobility developers ... Familiarity with machine learning algorithms and techniques Exposure to Python, Apache, HAProxy ...

Senior Machine Learning Engineer

Boston, MA ยท Hybrid

$107K - $199K/yr

Senior Machine Learning Engineer Job Duties: Design and implement image processing solutions to ... full range of statutory leaves of absence. We use data and analytics technologies, such as ...

Be it core Java, full-stack Java, Web/UI designers, Big Data or Cloud or Mobility developers ... Familiarity with machine learning algorithms and techniques Exposure to Python, Apache, HAProxy ...

Software Machine Learning Engineer

Reading, MA ยท On-site

$116.50 - $186.40/hr

Software Machine Learning Engineer Location: North Reading, MA, US Our Purpose TERADYNE, where experience meets innovation and driving excellence in every connection. We are fueled by creativity and ...

Machine Learning Engineer

Boston, MA ยท On-site

$136.32 - $225.09/hr

## Machine Learning EngineerApplyremote type: Hybridlocations ... Bostontime type: Full timeposted on: Posted Todayjob requisition id: R-058400\*Telecommuting ...

The team works across the project lifecycle and technical stack, from ideation, understanding and ... Successful candidates will have solid programming foundations, be comfortable translating between ...

Showing results 41-60

Internship Full Stack Machine Learning Engineer information

See Belmont, MA salary details

$49.1K

$148.8K

$210.3K

How much do internship full stack machine learning engineer jobs pay per year?

As of Aug 8, 2026, the average yearly pay for internship full stack machine learning engineer in Belmont, MA is $148,763.00, according to ZipRecruiter salary data. Most workers in this role earn between $122,500.00 and $174,400.00 per year, depending on experience, location, and employer.

What skills and qualifications are needed to thrive as an internship full stack machine learning engineer?

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 do internship full stack machine learning engineers do?

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 job categories do people searching Internship Full Stack Machine Learning Engineer jobs in Belmont, MA look for? The top searched job categories for Internship Full Stack Machine Learning Engineer jobs in Belmont, MA are:

Senior Machine Learning Engineer

C the Signs

Boston, MA โ€ข On-site, Remote

$133K - $175K/yr

Full-time

Re-posted 12 days ago


Job description

Position Summary
The Machine Learning Engineer will be responsible for the end-to-end development and deployment of Large language and machine learning models, with a primary focus on data preprocessing, model training, and fine-tuning using large-scale healthcare datasets. This role requires a strong understanding of Large language models, machine learning principles, data engineering, and experience working with sensitive healthcare data.
Key Responsibilities
  • Data Preprocessing: Clean, transform, and prepare large, complex healthcare datasets for machine learning model development. This includes handling missing values, outlier detection, feature engineering, and data normalization. Identify, collect, and curate relevant, industry-specific datasets for model retraining. Format data appropriately for the chosen LLM and training pipeline
  • Model Training & Fine-Tuning: Design, train, and fine-tune various LLMs on extensive healthcare data to solve specific clinical or operational problems. Set up and manage the training environment, including GPU instances and required software. Train and fine-tune pre-trained LLMs on the custom dataset to achieve specific goals. Experiment with and fine-tune hyperparameters such as learning rate, batch size, and training epochs to optimize model performance. Integration of structured + unstructured data (multi-modal/multi-input models)
  • Model Evaluation & Optimization: Evaluate model performance using appropriate metrics, identify areas for improvement, and implement optimization strategies.
  • Pipeline Development: Develop and maintain robust and scalable data and ML pipelines for model training, inference, and deployment.
  • Collaboration: Work closely with data scientists, clinicians, and software engineers to understand requirements, integrate models into production systems, and ensure data privacy and security compliance.
  • Research & Development: Stay up-to-date with the latest advancements in machine learning and healthcare AI, and explore new technologies and methodologies to enhance our solutions.
  • Documentation: Maintain clear and comprehensive documentation of models, data pipelines, and experimental results.

Requirements
  • Education: Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field.
  • Experience:
    • 5+ years of experience in Machine Learning Engineering or a similar role.
    • Proven experience with large-scale data preprocessing, LLM/model training, and fine-tuning.
    • Experience with distributed training (PyTorch Distributed, DeepSpeed, Ray, Hugging Face Accelerate).
    • Experience with GPU/TPU optimization, memory management for large language models.
    • Experience working with healthcare data is highly desirable.
  • Technical Skills:
    • Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy).
    • Strong understanding of various machine learning algorithms,Large Language Models, and deep learning architectures.
    • Experience with cloud platforms (e.g., GCP, AWS) and distributed computing frameworks (e.g., Spark) is a plus.
    • Familiarity with MLOps practices and tools.
  • Soft Skills:
    • Excellent problem-solving and analytical skills.
    • Strong communication and collaboration abilities.
    • Ability to work independently and as part of a team in a fast-paced environment.
  • Work Authorization:
      • Must be a US Citizen, Green Card holder, or currently in the US have valid H1B visa

Benefits
Why Join Us?
Joining C the Signs is not just about building AI; it's about shaping the future of healthcare. If you are a technical leader with an unshakable belief in the power of AI to save lives and the ability to make it happen at scale, this is your opportunity to create a tangible, global impact.
Benefits:
  • Competitive salary and benefits package.
  • Flexible working arrangements (remote or hybrid options available).
  • The opportunity to work on life-changing AI technology that directly impacts patient outcomes.
  • Join a team that combines cutting-edge innovation with a mission to save lives and improve health equity.
  • Continuous learning opportunities with access to the latest tools and advancements in AI and healthcare.