Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... The research intern will be in a fast-paced start-up environment playing a crucial technical role ...
Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... The research intern will be in a fast-paced start-up environment playing a crucial technical role ...
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Machine Learning Engineer Intern information
See Massachusetts salary details
$27.8K - $34.1K
5% of jobs
$36.1K is the 25th percentile. Wages below this are outliers.
$34.1K - $40.3K
59% of jobs
$40.3K - $46.5K
9% of jobs
$46.9K is the 75th percentile. Wages above this are outliers.
$46.5K - $52.7K
17% of jobs
$52.7K - $58.9K
4% of jobs
$58.9K - $65.1K
2% of jobs
$65.1K - $71.3K
3% of jobs
$71.3K - $77.5K
0% of jobs
$77.5K - $83.7K
0% of jobs
$83.7K - $89.9K
0% of jobs
$89.9K - $96.1K
0% of jobs
$27.8K
$46.5K
$96.1K
How much do machine learning engineer intern jobs pay per year?
What types of projects and tasks do Machine Learning Engineer Interns typically work on?
Machine Learning Engineer Interns are often involved in data preparation, feature engineering, model development, and performance evaluation under the guidance of senior engineers or data scientists. You may help implement and test machine learning algorithms, assist in cleaning and visualizing datasets, and contribute to code reviews or research tasks. Interns frequently collaborate with cross-functional teams, such as data scientists, software engineers, and product managers, to solve real-world problems and support ongoing projects. This hands-on experience provides valuable insights into the practical application of machine learning in a professional setting.
What is a Machine Learning Engineer Intern job?
A Machine Learning Engineer Intern is a temporary, entry-level role where individuals work with data scientists and engineers to develop, test, and optimize machine learning models. Interns typically assist in data preprocessing, feature engineering, model training, and evaluation. They may also work on improving existing algorithms, implementing research papers, or deploying models into production. This role provides hands-on experience with machine learning frameworks such as TensorFlow and PyTorch, as well as coding in Python and working with large datasets. The internship helps build practical skills and industry experience in artificial intelligence and data science.
What are the key skills and qualifications needed to thrive in the Machine Learning Engineer Intern position, and why are they important?
To thrive as a Machine Learning Engineer Intern, you need a solid understanding of programming languages such as Python, knowledge of machine learning algorithms, and experience with data analysis, typically supported by coursework in computer science or related fields. Familiarity with tools like TensorFlow, PyTorch, scikit-learn, and version control systems such as Git is often required. Strong problem-solving abilities, attention to detail, and effective communication are valuable soft skills in this role. These competencies enable interns to contribute meaningfully to projects, collaborate efficiently with teams, and adapt in a fast-paced, tech-driven environment.
Full-time
Posted 9 days ago
Job description
Our mission is to deliver the undeliverable.
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Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug delivery. The research intern will be in a fast-paced start-up environment playing a crucial technical role in generating cell culture and transfection data. The candidate will work with senior leadership and partner projects gaining broad internal and external exposure.
Essential Functions and Duties
- Design and implement complex data engineering processes to support innovative data science modeling
- Collaborate with chemistry and biology research teams to design data pipelines, analyze experimental data and implement experimentally actionable feed-back loops
- Apply and deploy established and novel statistical and machine learning algorithms to explore, understand and optimize properties of the vast delivery vehicle space, both in silico and experimentally
- Develop robust, scalable workflows and maintain security controls to protect sensitive data across cloud and on-premise environments
- Coordinate with cross-functional teams to deploy models and communicate results and with a focus on computational efficiency, performance, and usability
- Design of repositories, CI/CD pipelines and integration tests for ML workflows
Qualifications
MS in Computer Science, Data Science, Statistics, Computational Biology, Computational Chemistry, or a related discipline with 2 years hands-on machine learning experience.Â
Knowledge, Skills, and Abilities
- Track record developing statistical and machine learning models for complex and unconventional real-life problems
- Strong mathematical and coding skills
- Proficiency in Python, Â MLOps (W&B, MLFlow) and ML packages (scikit-learn, PyTorch, JAX), along with SQL and AWS.
- Familiarity with ML workflow best practices.
- Interest in applications of machine learning in biotechnology
- Strong communication skills, both written and verbal
- Experience doing research and working with interdisciplinary teams
Additional Preferred Experience (desired, but not essential):
- Experience in an industry setting related to biotechnology, chemicals, or materials manufacturing
- Experience with cheminformatics, computational chemistry, computational biology databases, data structures, material science and modelling package
Computer and modeling work required, this is an on-site position based in the Seaport of Boston, MA.
About Nanite
Sourced by ZipRecruiter
Industry
Biotechnology research and development
Company size
11 - 50 Employees
Headquarters location
Boston, MA, US
Year founded
2020