Research Intern

Duration: 3 months, with a possibility of a full-time job afterwards

Start date: immediately

• We're building state-of-the-art context compression. Our mission is to become the "Cloudflare for LLMs", a compression layer embedded into most LLM pipelines by default.

• We're a team of ex-EPFL MSc/PhDs. We started by publishing papers, then got into YC and started making money helping companies cut their LLM costs.

• We run the business like a research lab: form hypotheses, kill the ones that don't work, double down on the ones that do.

• Competitive compensation

• All the resources you need: GPUs, subscriptions, OpenAI/Anthropic credits

• As much responsibility as you can handle. Our goal is to make you an irreplaceable part of the team

• A fast-paced environment where you'll learn much faster than usual, surrounded by technical people who push each other

• Possibility of a full-time offer based on performance

• Hands-on supervision. We're around for brainstorming and high-level guidance, but you own your work and will be the person who knows it best.

• A well-defined project. We're early-stage and led by customer and market pull, so we work on several directions at once. You'll navigate this alongside the rest of us.

• Training wheels. After a short onboarding, you'll work on hard, customer-facing, time-sensitive problems like everyone else. Not a typical internship.

We're running a tight ship on a rough sea. Not for everyone, but you'll come out the other side a much stronger sailor.

1. You love research, read papers and hack on new repos for fun

2. Comfortable training ML models/transformers and doing independent applied research

3. Excellent Claude Code (or similar) user

4. Highly ambitious, ready for high-intensity YC startup culture, self-motivated

5. Strong communicator, fast response time, team player

1. LLM research experience, shown through publications, open-source contributions, or personal projects

2. BSc or MSc in CS/DS, math, or physics.

3. Startup or research internship experience (industry or academic)

1. A 40-minute call: 20 minutes for introductions and motivations, followed by 20 minutes of technical questions (mostly ML/LLM foundational questions)

2. A paid take-home project designed to take around 6 hours, followed by a 30-minute call to walk us through your work and answer a few questions

3. A 30-minute culture interview with the whole team

4. Offer