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Applied Research Intern Jobs (NOW HIRING)

Labelbox

Applied Research Intern

San Francisco, CA · On-site

Role Overview As an Applied Research intern at Labelbox, you will design, build, and productionize evaluation and posttraining systems for frontier LLMs and multimodal models. You'll own continuous ...

Labelbox

Applied Research Intern

San Francisco, CA · On-site

Role Overview As an Applied Research intern at Labelbox, you will design, build, and productionize evaluation and post-training systems for frontier LLMs and multimodal models. You'll own continuous ...

Huck Adventures

Research Analyst Intern

Boulder, CO · On-site

These teams are part of the Otter Applied Research Department to reduce paper consumption in the ... As a Research Intern, you will help us apply cutting-edge NLP algorithms to a wide range of media ...

Huck Adventures

Research Analyst Intern

Boulder, CO · On-site

These teams are part of the Otter Applied Research Department to reduce paper consumption in the ... As a Research Intern, you will help us apply cutting-edge NLP algorithms to a wide range of media ...

Purdue University

Student (4)

West Lafayette, IN · On-site

West lafayette Job Summary The Purdue Applied Research Institute, LLC (PARI) Microelectronics Lab is seeking a Microelectronics Research Intern to support PARI applied research initiatives during the ...

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How much do applied research intern jobs pay per month?

As of Jun 8, 2026, the average monthly pay for applied research intern in the United States is $6,439.50, according to ZipRecruiter salary data. Most workers in this role earn between $4,416.67 and $7,666.67 per month, depending on experience, location, and employer.

What does an Applied Research Intern do?

An Applied Research Intern assists in conducting experiments, analyzing data, and developing solutions for real-world problems under the guidance of experienced researchers. They often collaborate with research teams to design studies, collect and interpret data, and present findings. Their work bridges the gap between theoretical research and practical applications, helping to advance projects in fields such as technology, engineering, or science.

What types of projects do Applied Research Interns typically work on, and how do these contribute to the organization's goals?

Applied Research Interns often work on projects that involve exploring new technologies, developing prototypes, or conducting experiments to solve real-world problems relevant to the organization's mission. These projects may include data analysis, algorithm development, or creating proof-of-concept models, often under the guidance of senior researchers. Interns usually collaborate closely with both research and engineering teams, gaining exposure to interdisciplinary teamwork. Their contributions help bridge the gap between theoretical research and practical application, making a tangible impact on ongoing company initiatives.

What are the key skills and qualifications needed to thrive as an Applied Research Intern, and why are they important?

To thrive as an Applied Research Intern, you typically need strong analytical abilities, foundational knowledge in your research field, and progress toward a relevant degree such as in computer science, engineering, or a related discipline. Familiarity with data analysis tools, coding languages (such as Python or R), and research management software is often required. Curiosity, problem-solving, and effective communication are crucial soft skills for collaborating with teams and presenting findings. These skills and qualities are essential for conducting impactful research, generating actionable insights, and contributing meaningfully to projects.
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Applied Research Intern jobs near you
Applied Research Intern

Applied Research Intern

Labelbox

San Francisco, CA • On-site

Other

Posted 4 days ago

Job description

Role Overview

As an Applied Research intern at Labelbox, you will design, build, and productionize evaluation and posttraining systems for frontier LLMs and multimodal models. You'll own continuous, high-quality evals and benchmarks (reasoning, code, agent/tooluse, longcontext, visionlanguage, et al.), create and curate posttraining datasets (human + synthetic), and prototype RLHF/RLAIF/RLVR/RM/DPOstyle training loops to measure and improve realworld task and agent performance.

Your Impact
  • Build and own evaluation and benchmark suites for reasoning, code, agents, longcontext, and V/LLMs.
  • Create posttraining datasets at scale: design preference/critique pipelines (human + synthetic), and target hard failures surfaced by evals.
  • Experiment and prototype RLHF/RLAIF/RLVR/RM/DPOstyle training loops to improve real-world task and agent performance.
  • Land research in product: ship improvements into Labelbox workflows, services, and customerfacing evaluation/quality features; quantify impact with customer and internal metrics.
  • Engage with customer research teams: run pilots, codesign benchmarks, and share practical findings through internal research reports, blog posts, talks, and published papers.
What You Bring
  • A strong foundation in AI and machine learning, backed by a Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field (in progress degrees are acceptable for intern positions).
  • A deep understanding of frontier autoregressive and diffusion multimodal models, along with the human and synthetic data strategies needed to optimize them.
  • Passion and experience for LLM evaluation and benchmarking.
  • Expertise in training data quality construction, measurement and refinement.
  • The ability to bridge research and application by interpreting new findings and translating them into functional prototypes.
  • A track record of publishing in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL) and contributing to the broader research community.
  • Proficiency in Python and experience with deep learning frameworks like PyTorch, JAX, or TensorFlow.
  • Exceptional communication and collaboration skills.
Applied Research at Labelbox

At Labelbox Applied Research, we're committed to pushing the boundaries of AI and data-centric machine learning, with a particular focus on advancing human-AI interaction techniques. We believe that high-quality human data and sophisticated human feedback integration methods are key to unlocking the next generation of AI capabilities. Our research team works at the intersection of machine learning, human-computer interaction, and AI ethics to develop innovative solutions that can be practically applied in real-world scenarios.

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