About the role
Mirendil Mirendil is a tech-first company focused on solving core bottlenecks that unlock step-change acceleration across science and technology. Our first goal is to democratize frontier AI R&D across scientific disciplines. We believe accelerating scientific discovery is one of the most powerful ways to improve the future of humanity, and that AI will play a central role in making that possible. We are building a frontier AI research company and training our own models end-to-end. Our work spans areas such as model training, reinforcement learning, reasoning systems, and infrastructure for large-scale experiments. Our team includes researchers and engineers from Anthropic, Google DeepMind, xAI, OpenAI, Microsoft, Apple, and MIT.
The Role We are looking for an engineer to work at the intersection of research and systems on our pretraining stack. You'll contribute across the full pipeline, from data processing and model architecture to distributed training infrastructure and low-level optimization, and help determine how we scale our next generation of models. Some example areas you might work on (not limited to): Implement and iterate on model architectures, training algorithms, and optimizer research in large-scale pretraining runs Scale distributed training jobs across thousands of GPUs Optimize training throughput for novel attention mechanisms, architecture variants, and compute efficiency improvements Design and build large-scale data pipelines for efficient model consumption and dataset curation Run and analyze scientific experiments to advance understanding of how architecture and data choices affect model capabilities If you're excited about working across research and engineering to push the frontier of what large models can do, we'd love to hear from you. We offer a base salary of $350,000–$500,000 USD and a meaningful equity grant, depending on experience and background, along with competitive
benefits.
