Research Internship Reinforcement Learning (Summer)

Who are we?

Cohere is the leading security-first enterprise AI company.

We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems.

We’re training and deploying frontier models for enterprises who are building AI systems.

We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that.

We obsess over what we build.

Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers.

Cohere is a team of researchers engineers designers and more who are all passionate about their craft.

We are a global technology company co-headquartered in Toronto and San Francisco with key offices in London New York City Montreal Seoul Germany and Paris.

Join us!

Duration: Minimum 4 months (summer 2026 with potential extension) ABOUT THE PROJECT This internship offers a unique opportunity to contribute to cutting-edge research in reinforcement learning (RL) and large language models (LLMs) focusing on two interconnected projects: 1.

Combining Self-Distillation and Reinforcement Learning for LLMs with Applications to Code and Agentic Tasks This project explores how LLMs can improve through self-reflection and iterative learning by combining reinforcement learning with verifiable rewards (RLVR) and self-distillation.

The focus is on scenarios where structured feedback from verifiers compilers unit tests or tool calls enables models to detect errors revise outputs and learn from failures.

The internship will bridge theoretical mathematical modeling of self-distillation with practical production-oriented implementation. 2.

Dealing with Extremely Large Rollouts in RLVR As RLVR becomes a cornerstone for training reasoning-oriented LLMs the challenge of handling extremely large rollouts grows.

This project investigates mechanisms such as summarization memory context compaction hierarchical sub-agents and resumable rollouts to enable unbounded or very long trajectories.

It also explores how to effectively learn from such trajectories as traditional RLVR objectives fail when episodes exceed context window limits.

Both projects are grounded in recent research and aim to advance the state-of-the-art in LLM training and deployment.

Responsibilities - Conduct literature reviews and implement state-of-the-art algorithms in RL and self-distillation. - Design and execute experiments to evaluate the effectiveness of proposed methods on code generation and agentic tasks. - Develop and maintain codebases for both theoretical modeling and practical implementations. - Collaborate with researchers to analyze results refine methodologies and prepare findings for publication. - Contribute to the design of mechanisms for handling large rollouts such as summarization and hierarchical sub-agents. - Document progress methodologies and outcomes clearly and comprehensively.

REQUIREMENTS - Technical Skills: - Strong background in machine learning particularly reinforcement learning and deep learning. - Proficiency in Python and experience with ML frameworks (e.g.

PyTorch TensorFlow). - Familiarity with LLMs and their training paradigms. - Experience with coding tasks unit testing or compiler tools is a plus. - Educational Background: - Currently pursuing a Master’s or PhD in Computer Science Machine Learning or a related field. - Soft Skills: - Ability to work independently and manage complex projects. - Strong problem-solving and analytical skills. - Excellent communication skills for collaborating with a research team. - Additional: - Prior experience with RLVR self-distillation or large-scale ML experiments is highly desirable. - Willingness to learn and adapt to new methodologies and tools.

FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits including a separate budget for mental health. - RRSP matching 401K Pension Scheme. - 100% Parental Leave top-up for up to 6 months for either parent. - Annual enrichment benefits: Arts & culture fitness/wellness quality time and a workspace improvement credit.

Education & learning stipend for conferences courses and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote plus an annual company offsite.

HOW AND WHERE WE WORK: - Cohere is remote-friendly.

We have offices in Toronto San Francisco New York City London Paris Montreal and more coming soon. - For those in the office: a daily lunch program plenty of snacks and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly.

If any of the above doesn’t line up exactly with your experience we still encourage you to apply.

We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities.

Should you require any accommodations during the recruitment process please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit and we will work together to meet your needs.

We may use AI-enabled tools to screen and assess applicants against the criteria for this position.

This helps our recruiters identify potentially qualified candidates but it doesn't limit the applications our recruiters may review or consider.