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Internship Graduate Machine Learning Jobs in Burlington, VT

Grad Pharmacist

Morrisville, VT

$16.50 - $20.50/hr

... Interns play a critical role in supporting our pharmacy teams to consistently deliver on our brand ... As a Graduate Pharmacy Intern, you will apply your didactic learning from pharmacy school and ...

Grad Pharmacist

Morrisville, VT · On-site

$16.50 - $20.50/hr

... Interns play a critical role in supporting our pharmacy teams to consistently deliver on our brand ... As a Graduate Pharmacy Intern, you will apply your didactic learning from pharmacy school and ...

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Internship Graduate Machine Learning information

See Burlington, VT salary details

$25.6K

$42.8K

$88.3K

How much do internship graduate machine learning jobs pay per year?

As of Jul 15, 2026, the average yearly pay for internship graduate machine learning in Burlington, VT is $42,750.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,600.00 and $46,200.00 per year, depending on experience, location, and employer.

What is the difference between Internship Graduate Machine Learning vs Data Analyst?

AspectInternship Graduate Machine LearningData Analyst
Required CredentialsDegree in Computer Science, Data Science, or related field; basic knowledge of programming and statisticsDegree in Statistics, Mathematics, or related field; proficiency in data visualization and analysis tools
Work EnvironmentTech companies, research labs, startups; project-based, collaborative teamsBusiness, finance, marketing sectors; focus on reporting and data interpretation
Employer & Industry UsageUsed in tech, AI, and research industries for developing machine learning modelsCommon in corporate, finance, and consulting firms for data-driven decision making

While both roles involve working with data, an Internship Graduate Machine Learning focuses on developing algorithms and models using programming skills, often in tech environments. In contrast, a Data Analyst emphasizes interpreting data, creating reports, and supporting business decisions. The roles overlap in data handling but differ in technical depth and application focus.

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

To thrive as an Internship Graduate in Machine Learning, you typically need a strong background in mathematics, programming (especially Python), and familiarity with algorithms and data structures, often supported by coursework or a degree in computer science, statistics, or a related field. Hands-on experience with machine learning frameworks like TensorFlow or PyTorch, and knowledge of tools such as Jupyter Notebooks and version control systems like Git, are highly valued. Curiosity, problem-solving, teamwork, and effective communication are crucial soft skills to excel in collaborative and innovative environments. These competencies enable interns to contribute to real-world projects, adapt to fast-changing technologies, and communicate findings clearly within interdisciplinary teams.

What are Internship Graduate Machine Learning positions?

Internship Graduate Machine Learning positions are entry-level roles designed for recent graduates or students who have completed coursework in machine learning, data science, or related fields. These internships provide hands-on experience working with real-world data, building and testing machine learning models, and collaborating with experienced professionals. Interns gain exposure to industry-standard tools and techniques, helping them bridge the gap between academic learning and practical application. Such positions are valuable for building a portfolio, networking, and enhancing job prospects in the rapidly growing field of artificial intelligence.

What types of projects do Internship Graduate Machine Learning roles typically involve, and how are responsibilities structured within the team?

Internship Graduate Machine Learning roles often focus on supporting ongoing research or development projects, such as building predictive models, cleaning and analyzing data, or prototyping algorithms. Interns usually collaborate closely with data scientists and engineers, contributing to specific project milestones while learning best practices in model development and deployment. Responsibilities are often structured to allow for mentorship and feedback, with interns participating in regular team meetings, code reviews, and brainstorming sessions. This collaborative environment provides valuable exposure to real-world machine learning workflows and helps interns build both technical and soft skills relevant to the field.
What job categories do people searching Internship Graduate Machine Learning jobs in Burlington, VT look for? The top searched job categories for Internship Graduate Machine Learning jobs in Burlington, VT are:
Post Doctoral Associate in Civil and Environmental Engineering at UVM

Post Doctoral Associate in Civil and Environmental Engineering at UVM

The University of Vermont

Burlington, VT • On-site

Full-time

Re-posted 22 days ago


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Job description

Post Doctoral Associate in Civil and Environmental Engineering at UVM
Posting Summary
Join a vibrant research community at the University of Vermont as a Postdoctoral Scholar. UVM is an R1 research university guided by Our Common Ground values that prioritizes transdisciplinary research and collaboration as a strategy for continued strengthening of scientific inquiry and education. Burlington is a vibrant community located on the shores of Lake Champlain, between the Adirondack and Green Mountains. With year-round recreational opportunities, safe neighborhoods and excellent schools, this progressive community has been frequently cited as being one of the healthiest and best cities in the US in which to live.
The Postdoctoral Scholar will play a key role in advancing NSF AQUA - CLIME convergence research under the supervision and mentorship of Dr. Raju Badireddy in the Department of Civil and Environmental Engineering. The position involves laboratory- and field-oriented research focused on developing and deploying novel low-cost microsensors to investigate how climate-driven disturbances- including flooding, droughts, and wildfires- affect the water quality across diverse hydroclimatic conditions, ranging from humid to arid regions. The project align with our institution's vision to pursue world-class research develop leaders in science and engineering, fostering transdisciplinary collaboration and impactful discoveries.
Research Objectives: The project tests two central hypotheses: (1) that flood, droughts, and wildfires generate substantially different nutrient and emerging contaminant sources, sinks, and transport pathways compared to similar events in other seasons; and (2) that increasingly frequent floods alter watershed function in ways that reduce nutrient retention and other contaminants. Research will build on previous work on developing low-cost nutrient electrochemical microsensors and will leverage two to three heavily instrumented Vermont watersheds spanning a range of land uses, with particular emphasis on how export pathways and processes vary seasonally.
Responsibilities: A central component of this work involves both laboratory and field validation of novel microsensors, which will be deployed alongside standard in-stream sensors and transitional grab-sampling methods. The Scholar will develop proficiency in operating a broad range of high-frequency sensing platforms and in analyzing large environmental datasets to generate insights into watershed functioning under changing climate scenarios.
Qualifications: The candidates must have a Ph.D. in Environmental Engineering or related field by the start date and demonstrate expertise in fabricating and testing microsensors for environmental contaminants in water and soil systems. Specifically, the position seeks candidates with a strong background in environmental engineering, electrochemistry, material science, environmental nanotechnology, and electronics focusing on in-situ chemical and physical sensing technologies, as is proficiency in advanced statistical analysis- particularly in R or similar- for interpreting large environmental datasets. Ideal candidates will have a robust publication record, experience with interdisciplinary projects, and a proven ability to foster inter-institutional collaborations. Additionally, they will have experience guiding undergraduate and graduate students on transformative research and high-impact journal publications, with a commitment to education, outreach, and collaborations. The candidate will have ability to effectively communicate complex concepts to diverse stakeholders.
Research Community: The successful candidate will join our cutting-edge, transdisciplinary research community (35 plus members from Vermont, South Dakota, and New Mexico) working across sensor development and distributed sensor networks, climate modeling, and Artificial Intelligence/Machine Learning. The position also offers meaningful engagement with academic, industry, farming communities and Native Americans Communities across three jurisdictions: Vermont, South Dakota, and New Mexico.
Appointment: This is a one-year position with the possibility of a one-year extension contingent on satisfactory progress. The annual salary is competitive and commensurate with federal agency guidelines, accompanied by an excellent benefits (https://www.uvm.edu/hrs/postdoctoral-associates-fellows-overview). The position provides opportunities for professional development, including media and communications training. The expected start date is August 1, 2026.
Minimum Qualifications (or equivalent combination of education and experience)
The candidates must have a Ph.D. in Environmental Engineering or related field by the start date and demonstrate expertise in fabricating and testing microsensors for environmental contaminants in water and soil systems. Specifically, the position seeks candidates with a strong background in environmental engineering, electrochemistry, material science, environmental nanotechnology, and electronics focusing on in-situ chemical and physical sensing technologies, as is proficiency in advanced statistical analysis- particularly in R or similar- for interpreting large environmental datasets. Ideal candidates will have a robust publication record, experience with interdisciplinary projects, and a proven ability to foster inter-institutional collaborations. Additionally, they will have experience guiding undergraduate and graduate students on transformative research and high-impact journal publications, with a commitment to education, outreach, and collaborations. The candidate will have ability to effectively communicate complex concepts to diverse stakeholders.
Desirable Qualifications
Anticipated Pay Range
$63,480 - $77,076
Other Information
Support departmental initiatives, assist with occasional teaching or guest lecturing, serve on committees, or attend training sessions as appropriate.
Special Conditions
Contingent on continued funding, Background Check required for this position

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