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Data Engineer Internship Jobs in Berkeley, CA (NOW HIRING)

Armaninollp

Data & AI Platform Engineer

San Francisco, CA · On-site

$134K - $162K/yr

Minimum 2 years in a data engineering, platform engineering, analytics engineering, or cloud operations role (internships/co-ops count). * Minimum 1 year of representative accounting experience ...

Armaninollp

Data & AI Platform Engineer

San Ramon, CA · On-site

$128K - $153K/yr

Minimum 2 years in a data engineering, platform engineering, analytics engineering, or cloud operations role (internships/co-ops count). * Minimum 1 year of representative accounting experience ...

Armanino LLP

Data & AI Platform Engineer

San Ramon, CA · On-site

$128K - $153K/yr

Minimum 2 years in a data engineering, platform engineering, analytics engineering, or cloud operations role (internships/co-ops count). * Minimum 1 year of representative accounting experience ...

DEUNA

Data Science intern

San Francisco, CA · On-site

During the internship, you will support the Data team by analyzing data, building models ... Collaborate with Data Scientists, Data Engineers, and Product teams on ongoing initiatives.

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All JobsData Engineer JobsData Engineer Internship JobsData Engineer Internship Jobs in Berkeley, CA

Data Engineer Internship information

See Berkeley, CA salary details

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How much do data engineer internship jobs pay per hour?

As of Sep 4, 2026, the average hourly pay for data engineer internship in Berkeley, CA is $31.12, according to ZipRecruiter salary data. Most workers in this role earn between $25.29 and $35.34 per hour, depending on experience, location, and employer.

What is a data engineer internship?

A Data Engineer Internship is a temporary, entry-level role where interns gain hands-on experience in data infrastructure, ETL pipelines, and database management. Interns typically work with large datasets, assist in building data models, and collaborate with data scientists and analysts to ensure efficient data processing. They learn tools like SQL, Python, and cloud platforms while improving data quality and automation processes. This role provides valuable industry experience and prepares interns for full-time data engineering positions.

What types of projects or tasks can I expect to work on during a data engineer internship?

As a Data Engineer Intern, you can expect to assist with building and maintaining data pipelines, cleaning and transforming datasets, and supporting the integration of new data sources. You may also help optimize database performance, troubleshoot data quality issues, and collaborate with data scientists or analysts to ensure data accessibility. Interns often work on real-world projects that offer hands-on experience with tools and technologies common in the industry. This exposure not only builds your technical skills but also provides valuable insights into how data engineering supports business decision-making and analytics.

What are the key skills and qualifications needed to thrive in a data engineer internship, and why are they important?

To thrive as a Data Engineer Intern, you need a solid background in programming (especially Python or Java), SQL, and basic data management concepts, often gained through coursework in computer science, data science, or related fields. Experience with tools like SQL databases, ETL pipelines, and cloud platforms (such as AWS or Azure), as well as familiarity with big data frameworks like Hadoop or Spark, is highly valuable. Strong analytical thinking, attention to detail, and the ability to communicate technical information clearly help interns collaborate effectively within diverse teams. These skills are vital for supporting data infrastructure development, ensuring data quality, and contributing to impactful data-driven solutions.

What does a data engineer intern do?

A data engineer intern assists in designing, building, and maintaining data pipelines and infrastructure to support data analysis and storage. They often work with tools like SQL, Python, and cloud platforms, gaining experience in data processing, database management, and data warehousing under supervision.

What are the most commonly searched types of Data Engineer jobs in Berkeley, CA?

The most popular types of Data Engineer jobs in Berkeley, CA are:

What are popular job titles related to Data Engineer Internship jobs in Berkeley, CA?

For Data Engineer Internship jobs in Berkeley, CA, the most frequently searched job titles are:

What job categories do people searching Data Engineer Internship jobs in Berkeley, CA look for?

The top searched job categories for Data Engineer Internship jobs in Berkeley, CA are:

What cities near Berkeley, CA are hiring for Data Engineer Internship jobs?

Cities near Berkeley, CA with the most Data Engineer Internship job openings:

Data Engineer Internship jobs near you
Infographic showing various Data Engineer Internship job openings in Berkeley, CA as of August 2026, with employment types broken down into 21% Internship, and 79% Full Time. Highlights an 90% In-person, and 10% Remote job distribution, with an average salary of $64,732 per year, or $31.1 per hour.

AI Engineer, Internship - Summer 2026 - Applications Open Now

Postman

Berkeley, CA • On-site

Temporary, Internship

Re-posted 2 days ago

Job description

The Opportunity (Summer 2026 AI Internship - Applications Open Now)

We're seeking an AI Engineer Intern to work alongside our AI team on large-scale AI and Agentic  systems from data pipeline to production deployment. This role is scoped for someone with foundational experience who wants to deepen it: you'll own discrete pieces of real systems under the mentorship of senior engineers, not shadow work or isolated coursework-style projects.

What You'll Do

You'll work directly with the AI team, taking responsibility for well-scoped pieces of real systems, with mentorship from senior engineers.

Benchmarks & Evaluation

  • Contribute to APIFlow-Bench, our open-source benchmark for real API-development work: design and review benchmark tasks and their mock API environments, extend the evaluation harness and task-generation pipeline in Python, and help maintain the public multi-model leaderboard with statistical confidence intervals.
  • Help build a new action-level AI safety benchmark: instead of grading what a model says, it scores what an agent actually does inside a simulated enterprise API environment. You'll work on scenario design, threat modeling (prompt injection, data exfiltration, permission overreach), and auditable evaluation design.

Model Training & Efficiency

  • Fine-tune open-weight models for tool calling and agentic tasks (SFT, distillation, and RL) using PyTorch and the open-source training ecosystem, on both managed training platforms and self-managed cloud GPUs.
  • Design and run experiments with rigor: evaluate every training run on our benchmarks, support ablation studies and error analysis, track experiments, and report results honestly, including cost.
  • Evaluate ultra-low-bit quantized models for on-device use: extend our quantized vs. full-precision benchmark comparisons and analyze where and why they diverge.

Agent Systems & Engineering Practice

  • Help build the next generation of Postman's in-product AI agent (Agent Mode): a deliberately minimal agent architecture that calls LLM APIs directly (tool loops, multi-step execution, checkpointing), primarily in TypeScript. No prior TypeScript is required; strong Python fundamentals transfer quickly.
  • Read the source code of open-source agent harnesses and turn what you learn into design specs and prototypes.
  • Document experiments, design decisions, and runbooks so your work is legible to the next person; flag safety, fairness, or privacy concerns you observe in model or agent behavior.
About You
  • Currently pursuing a BS, MS, or PhD in Computer Science, Data Science, or a related quantitative field.
  • Hands-on experience training or evaluating ML models: course projects, research, hackathons, or a prior internship all count.
  • Solid Python fundamentals: data structures, functions, basic testing; comfortable writing and reviewing code outside of notebooks.
  • Working knowledge of at least one deep-learning framework (PyTorch preferred).
  • Clear written and verbal communication, and a habit of documenting what you build.
Preferred Qualifications  (none required; the more of these you have, the better)
  • Experience fine-tuning open-weight LLMs (SFT, LoRA, RL, or distillation), with the improvement measured on a benchmark.
  • Experience building LLM agents (tool calling, multi-step loops) or LLM evaluation harnesses/benchmarks, and reporting results with statistical rigor.
  • A track record of shipping real software end-to-end: APIs and services, CLIs, Docker, CI/CD, cloud; public code on GitHub is a big plus.
  • Interest or experience in AI safety and robustness: red-teaming, prompt injection, agent security, fairness, or interpretability.
  • Exposure to model-efficiency work: quantization, low-bit inference, or serving optimization.
  • Evidence of rigor and initiative: publications, technical blog posts, ablation studies, or self-driven side projects with quantified results.
  • Fluency with AI coding tools (Claude Code, Cursor, Codex) to ship fast while still deeply understanding the systems you build.

Apply