INTERNSHIP DETAILS

Engineer Intern, GenAI Research (Summer Internship)

CompanyAppen
LocationUnited States
Work ModeRemote
PostedFebruary 13, 2026
Internship Information
Core Responsibilities
The role involves designing and implementing research and engineering workflows to strengthen model performance, create new benchmarks, and improve production models. Responsibilities include hands-on ownership of training and evaluation pipelines, benchmark development, and model improvement initiatives that directly influence deployed systems.
Internship Type
intern
Company Size
19693
Visa Sponsorship
No
Language
English
Working Hours
40 hours
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About The Company
Appen has been a leader in AI training data for over 25 years, providing high-quality, diverse datasets that power the world's leading AI models. Our end-to-end platform, deep expertise, and scalable human-in-the-loop services enable AI innovators to build and optimize cutting-edge models. We specialize in creating bespoke, human-generated data to train, fine-tune, and evaluate AI models across multiple domains, including generative AI, large language models (LLMs), computer vision, speech recognition, and more. Our solutions support critical AI functions such as supervised fine-tuning, reinforcement learning with human feedback (RLHF), model evaluation, and bias mitigation. Our advanced AI-assisted data annotation platform, combined with a global crowd of more than 1M contributors in over 200 countries, ensures the delivery of accurate and diverse datasets. Our commitment to quality, scalability, and ethical AI practices makes Appen a trusted partner for enterprises aiming to develop and deploy effective AI solutions. At Appen, we foster a culture of innovation, collaboration, and excellence. We value curiosity, accountability, and a commitment to delivering the highest-quality AI solutions. We support work-life balance with flexible work arrangements and a dynamic, results-driven environment. Employees have access to competitive pay, comprehensive benefits, and opportunities for continuous learning and career growth. Our team works closely with the world’s top technology companies and enterprises, tackling exciting challenges and shaping the future of artificial intelligence.
About the Role

Why Join This Team

Appen’s GenAI research team advances how frontier models are evaluated, improved, and deployed in production environments.


The purpose of this role is to design and implement research and engineering workflows that strengthen model performance, create new benchmarks, and improve production models without regressing on core characteristics.


This role provides hands on ownership of training and evaluation pipelines, benchmark development, and model improvement initiatives that directly influence deployed systems.

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Your Impact
  • Design and implement a lightweight supervised fine tuning training pipeline using open source LLMs.
  • Create new benchmarks to evaluate frontier models across defined scientific and performance criteria.
  • Analyze production models to identify measurable areas for improvement.
  • Improve model performance through targeted retraining and hyperparameter search.
  • Deploy improved models while maintaining core model characteristics and avoiding regression.
  • Build Python tooling to automate training, evaluation, benchmarking, and experimentation workflows.
  • Implement structured evaluation methods, including rubric based scoring and LLM as a judge workflows.
  • Document experimental design, benchmark methodology, and performance results with clarity and precision.
  • Iterate rapidly in a research driven environment to increase model quality and reliability.


What You Bring
  • Current enrollment in or recent completion of a Master’s or PhD in Computer Science, AI, Machine Learning, Computer Engineering, or a closely related technical field.
  • Strong experience working with large language models, including supervised fine tuning, prompt engineering, or model evaluation.
  • Hands on experience building machine learning pipelines or research infrastructure.
  • Experience improving model performance through retraining or hyperparameter tuning.
  • Proficiency in Python and comfort working with machine learning frameworks and open source model ecosystems.
  • Familiarity with cloud environments such as AWS or Azure.
  • Strong technical problem solving ability, including use of LLMs as development aids for building and iteration.
  • Ability to work independently with minimal hand holding.
  • Strong written communication skills for summarising research and drafting technical documentation.
  • Ability to collaborate effectively in a remote research environment.


Additional Details
  • Duration: June-August
  • Schedule: Full-time
  • Work Type: Remote


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Why You'll Love Working Here


At Appen, we foster a culture of innovation, collaboration, and excellence. We value curiosity, accountability, and a commitment to delivering the highest quality AI solutions for frontier models.

You’ll work on complex challenges that shape the future of AI across industries and geographies, alongside talented people in a culture that values humility over ego. You’ll have the flexibility to deliver in a way that works for you and your team, supported by tools, resources and development opportunities to continue to build your capability over time.


About Appen


Appen has been a leader in AI training data for over 30 years. We specialise in human generated data to train, fine tune, and evaluate models across generative AI, large language models, computer vision, and speech recognition. Our AI assisted data annotation platform and global crowd of more than 1 million contributors in over 200 countries support model pre training, supervised fine tuning, evaluation and benchmarking, safety and red teaming, and multilingual global expansion.

Key Skills
Supervised Fine TuningPrompt EngineeringModel EvaluationMachine Learning PipelinesHyperparameter TuningPythonAWSAzureLLM as a JudgeRubric Based ScoringResearch Driven EnvironmentTechnical DocumentationLarge Language ModelsOpen Source LLMsBenchmarkingExperimentation Workflows
Categories
Science & ResearchEngineeringData & AnalyticsSoftware