Engineer Intern, GenAI Research (Summer Internship)

Appen

6d • Remote

About The Position

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.

Requirements

• 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.

Responsibilities

• 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 This Job Offers

• Job Type: Full-time
• Career Level: Intern