Research Intern

View all jobsResearch InternLondonEngineeringIn officeInternAbout AtlaAtla is committed to engineering safe, beneficial AI systems that will have a massive positive impact on the future of humanity. We are a London-based start-up building the most capable AI evaluation models. Become part of our growing world-class team, backed by Y Combinator, Creandum, and the founders of Reddit, Cruise, Rappi, Instacart and more.RoleAs Atla’s research intern, you will collaborate with our researchers and obtain deep experience in a growing AI startup. As part of your role, you will:Conduct cutting-edge machine learning research, contributing to research initiatives that have practical applications in our product development.Disseminate your research results through the production of publications, datasets, and code.Our ongoing research projects encompass but are not limited to:Iterative Self ImprovementThis project applies iterative self-improvement to enhance our general-purpose evaluator. This involves using the model’s outputs to refine its training data iteratively, rather than relying on fixed datasets. Prior work [1, 2, 3, 4] demonstrates the effectiveness of this approach, and we aim to extend it to evaluation systems.We will leverage our internal training data, infrastructure, and benchmarks to iteratively refine the evaluator. You will collaborate with engineers to build infrastructure for iteratively generating better and more informative data. Techniques from our research on techmulti-stage synthetic data generation will be incorporated to improve data quality.Key challenges include addressing bias amplification, semantic drift, and maintaining diversity of data to ensure model stability and alignment. This project aims to advance safe iterative training methodologies and deliver a more capable evaluator, with findings targeted for a top-tier conference. The scope can be tailored to your skills and interests.[1] Wang, Y., et al. (2023). SELF-INSTRUCT: Aligning Language Models with Self-Generated Instructions.[2] Yuan, W., et al. (2024). Self-Rewarding Language Models.[3] Wang, T., et al. (2024). Self-Taught Evaluators.[4] Li, X., et al. (2024). MONTESSORI-INSTRUCT: Generate Influential Training Data Tailored for Student Learning.Inference Time ComputeThis project explores inference-time compute scaling to enhance our general-purpose evaluator, particularly for complex tasks like coding, which benefit from longer reasoning chains. Recent research [1, 2] has shown the effectiveness of inference-time compute in improving performance on reasoning and mathematical tasks by leveraging more tokens during inference.We will investigate methods to train models capable of utilising additional tokens effectively for reasoning. This involves experimenting with reinforcement learning (RL) approaches, such as group reinforcement policy optimisation (GRPO), to encourage self-verification and reasoning strategies. You will work with engineers to develop the necessary training infrastructure.Key challenges include addressing trade-offs between token efficiency and performance while mitigating common issues. The project aims to develop robust methods for inference-time compute scaling and contribute findings to a top-tier conference. The scope can be tailored to your skills and interests.[1] Guo, D., et al. (2025). DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.[2] Snell, C., et al. (2024). Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.Agentic EvaluationThis project investigates how to evaluate agentic systems using an LLM-as-a-Judge framework. Agents introduce new challenges due to their ability to reason, plan, and interact with external tools [1,2]. Evaluating their capabilities and safety requires new approaches, with potential directions including:Agent-as-a-Judge: Using agentic systems to evaluate other agentic systems, reducing reliance on human judgment and...