Machine Learning Researcher - Engineer Foundational Models H/F Pathway

- Type of contract: Full-time, permanent
- Preferable joining date: Immediate. The positions are open until filled - please apply immediately.
- Compensation: six-digit annual salary based on profile and location + Employee Stock Option Plan.
- Location: Remote work. Possibility to work or meet with other team members in one of our offices: Palo Alto, CA; Paris, France or Wroclaw, Poland. Candidates based anywhere in the EU, UK, United States, and Canada will be considered.

If you meet our broad requirements but are missing some experience, don't hesitate to reach out to us.

Cover letter

It's always a pleasure to say hi! If you could leave us 2-3 lines, we'd really appreciate that.

You are expected to meet at least one of the following criteria:
- You have published at least one paper at NeurIPS, ICLR, or ICML - where you were the lead author or made significant conceptual & code contributions.
- You have significantly contributed to an LLM training effort which became newsworthy (topped a Huggingface benchmark, best in class model, etc.), preferably using multiple GPU's.
- You have spent at least 6 months working in a leading Machine Learning research center (e.g. at: Google Brain / Deepmind, Apple, Meta, Anthropic, Nvidia, MILA).
- You were an ICPC World Finalist, or an IOI, IMO, or IPhO medalist in High School.

You Are

- A deep learning researcher, with a track record in Language Models and/or RL (candidates with a Vision or Robotics ML background are also welcome to apply).
- Interested in improving foundational architectures and creating new benchmarks.
- Experienced at hands-on experiments and model training (PyTorch, Jax, or Tensorflow).
- Have a good understanding of GPU architecture, memory design, and communication.
- Have a good understanding of graph algorithms.
- Have some familiarity with model monitoring, git, build systems, and CI/CD.
- Respectful of others
- Fluent in English

Bonus Points

- Knowledge of approaches used in distributed training.
- Familiarity with Triton
- Successful track-record in algorithms & data science contests.
- Showing a code portfolio.

Why You Should Apply

- Join an intellectually stimulating work environment.
- Be a pioneer: you get to work with a new type of "Live AI" challenges around long sequences and changing data.
- Be part of one of an early-stage AI startup that believes in impactful research and foundational changes.