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Machine Learning Engineer Intern Jobs in Malvern, PA

Athenago

AI Engineering Intern

Wilmington, DE

Role Overview The AI Engineer Intern will develop and enhance Large Language Models (LLMs) to ... Solid understanding of machine learning fundamentals and algorithms (classification, NLP, Deep ...

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All JobsMachine Learning Engineer Intern JobsMachine Learning Engineer Intern Jobs in Malvern, PA

Machine Learning Engineer Intern information

See Malvern, PA salary details

$25K

$41.8K

$86.4K

How much do machine learning engineer intern jobs pay per year?

As of Aug 12, 2026, the average yearly pay for machine learning engineer intern in Malvern, PA is $41,786.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,900.00 and $45,100.00 per year, depending on experience, location, and employer.

What do machine learning engineer interns do?

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?

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 skills and qualifications are needed to thrive as a machine learning engineer intern?

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.

What are popular job titles related to Machine Learning Engineer Intern jobs in Malvern, PA? For Machine Learning Engineer Intern jobs in Malvern, PA, the most frequently searched job titles are:
What cities near Malvern, PA are hiring for Machine Learning Engineer Intern jobs? Cities near Malvern, PA with the most Machine Learning Engineer Intern job openings:
Machine Learning Engineer Intern jobs near you
Infographic showing various Machine Learning Engineer Intern job openings in Malvern, PA as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 22% Part Time, 1% Temporary, and 3% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $41,786 per year, or $20.1 per hour.

Principal Data & Machine Learning Engineer

AKUVO LLC

Malvern, PA • On-site

$158K - $216K/yr

Full-time

Posted 20 days ago

Job description

THE OPPORTUNITY

AKUVO is seeking a Principal Data & Machine Learning Engineer to serve as the senior-most technical owner across AKUVO’s data platform, machine-learning models, and the services behind AKUVO IQ. This is a breadth role: you are equally at home building production applications and APIs, engineering the data lake and infrastructure, and developing and deploying predictive models — the person the team turns to at any layer.

You will lead the technical execution of the data and analytics strategy, own architecture across data engineering and machine learning, internalize critical systems currently held by external partners, and provide technical leadership and mentorship to the engineering team. The role combines hands-on engineering across the full stack with technical leadership and direct ownership of production systems.

LOCATION

Local in Malvern/Philadelphia first, widening to surrounding areas such as New Jersey, New York, Delaware, while continuing to expand geographically in a hybrid/remote capacity based on location.

KEY RESPONSIBILITIES

  • Lead the technical execution of the data and analytics strategy across data engineering and machine learning, and own the architecture for AKUVO’s data lake, ML platform, model pipelines, and the data services behind AKUVO IQ.
  • Work hands-on across the full stack — application and API development, systems and infrastructure, data pipelines, and predictive-model development — stepping directly into whichever layer the team needs.
  • Build, deploy, and maintain predictive models and scores alongside the Senior Data & Machine Learning Engineer, contributing directly to model development as well as the platform beneath it.
  • Internalize critical data and ML systems currently held by external partners through a structured knowledge-transfer and documentation process, building internal depth and reducing concentration risk.
  • Design scalable, reliable, and secure architectures for structured portfolio data, predictive-model data, and separately governed PII and AI-conversation data.
  • Own the operational disciplines for pipelines and production models — monitoring, alerting, incident response, versioning, drift detection, and retraining — so systems can be independently deployed, monitored, and enhanced.
  • Evolve technical practices for architecture, development, testing, CI/CD, observability, documentation, and data quality, and ensure data is accurate, timely, and traceable with clear lineage and governance.
  • Provide technical leadership, mentorship, and development to the engineering team, set technical direction, and coordinate delivery.
  • Partner with Applied AI, the Collections domain, Product, Engineering, Architecture & Innovation, and Compliance to keep data, models, and AI systems integrated, governed, and production-ready.
  • Evaluate technical investments, cost, and resource needs; make pragmatic build-versus-buy decisions; and document and prioritize key risks, dependencies, and technical debt.
  • Communicate architecture, risks, and priorities clearly to executive and cross-functional stakeholders, and advance AI-assisted engineering practices across the team.

SKILLS AND EXPERIENCE

  • 10+ years across software/data engineering and machine learning, with hands-on delivery spanning application development, systems and infrastructure, data platforms, and production ML models.
  • 3+ years providing technical leadership and developing engineers.
  • Full-stack breadth — able to build applications and APIs, engineer data pipelines and infrastructure, and develop, deploy, and maintain ML models; the person the team relies on at any layer.
  • Deep, hands-on experience with cloud data and ML platforms in production (Azure strongly preferred) — data lakes, layered architectures, pipelines, product-serving APIs, and model pipelines.
  • Strong Python and SQL, and modern engineering practices (ETL/ELT, CI/CD, observability, testing, environment management).
  • A track record of internalizing critical systems and knowledge through structured transitions, and of setting and evolving technical practices.
  • Ownership of the production model lifecycle — deployment, versioning, monitoring, drift detection, and retraining.
  • Proven ability to translate business and product priorities into scalable roadmaps and pragmatic build-versus-buy decisions.
  • Strong communication with executive, product, and cross-functional stakeholders, and comfort operating as a hands-on technical leader.
  • Active, sophisticated use of AI within your own engineering and leadership workflow.

PREFERRED QUALIFICATIONS

  • Experience spanning both software/platform engineering and applied ML in the same role — a rare full-stack-plus-modeling breadth.
  • Microsoft Fabric and OneLake, or experience leading a Synapse-to-Fabric migration; Databricks or comparable ML platforms.
  • B2B SaaS, fintech, or financial-services background (2+ years), ideally with collections, lending, or credit-scoring exposure.
  • Experience standing up or maturing model governance, documentation, and compliance practices.
  • Experience with sensitive, PII, or regulated data and separately governed data zones.
  • Azure DevOps and structured delivery processes (Epics → Features → Stories → Tasks).