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# Machine Learning Researcher / Engineer (Foundation Models)

- **Company:** Ignite, LLC
- **Location:** Palo Alto, CA, United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Continuous Integration, Data Cleansing, Distributed Computing Environment, Monitoring of Systems, Machine Learning, Language Modeling, Tensorflow, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Deep Learning, Git, HuggingFace, Build Tools, DeepMind
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25303_40b568af1cf597ebd713e3a6315db5c8

## About the Role

1. You have published at least one paper at NeurIPS, ICLR, or ICML - where you were the lead author or made significant conceptual & code contributions.
 2. 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.
 3. 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).
 4. You were an ICPC World Finalist, or an IOI, IMO, or IPhO medalist in High School., * 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, * Knowledge of approaches used in distributed training.
 * Familiarity with Triton
 * Successful track-record in algorithms & data science contests.
 * Showing a code portfolio.

## Description

We are currently searching for 1 or 2 R&D Engineers with a strong track record in machine learning models research.

This is an extremely ambitious foundational project. There is a flexible GPU budget associated with this specific project, guaranteed to be in the 7-digit range minimum.

You Will

 * perform (distributed) model training.
 * help improve/adapt model architectures based on experiment results.
 * design new tasks and experiments.
 * optionally: oversee activities of team members involved in data preparation.

The results of your work will play a crucial role in the success of the project.