appen-2 - Appen - Engineer Intern, GenAI Research (Summer Internship)
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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. • ## Additional Details • Duration: June-August • Schedule: Full-time • Work Type: Remote
Benefits
• 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. • 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. • 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.
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