Remote Engineer

We are partnering with a well-funded, rapidly scaling deep-tech company operating at the intersection of advanced AI and next-generation computing to find their next Senior LLM Engineer. Backed by strong commercial traction and global enterprise clients, the company is building highly efficient, production-grade AI systems designed to solve complex real-world problems at scale. Their team combines world-class researchers and engineers working on cutting-edge challenges in large-scale model development, optimization, and deployment. This is an opportunity to join a highly technical environment where you will directly contribute to the future of large language models - not just apply them. As a Senior LLM Engineer, you will design, train, and optimize large-scale transformer models, contributing across pretraining, post-training alignment (SFT, RLHF, DPO), evaluation, and inference optimization. This is a deeply technical role focused on core model development rather than downstream application or prompt engineering. Key Responsibilities Design and train transformer-based models from scratch, including large-scale pretraining pipelines Contribute to post-training workflows such as SFT, RLHF, and DPO Build and optimize large-scale data pipelines for training and evaluation Improve model performance through architecture, training, and efficiency optimizations Optimize inference and training performance across GPU/HPC environments Collaborate with engineering teams to deploy models into production systems Mentor junior engineers and contribute to technical best practices

2+ years of hands-on experience training transformer or LLM models from scratch 5+ years overall experience for Senior-level candidates across deep learning applications Strong understanding of transformers, optimization, and deep learning fundamentals Expertise in Python, PyTorch, and the Hugging Face ecosystem Experience with distributed training frameworks such as DeepSpeed, FSDP, or Megatron Familiarity with inference optimization tools such as vLLM or TensorRT-LLM Experience working with large datasets, scalable training pipelines, and GPU optimization

Influence next-generation AI systems at scale Join a highly technical, research-driven environment with real-world impact Competitive compensation, flexible working, and strong growth potential Recruiter's Note We are specifically targeting engineers who have built and optimized models themselves-not candidates focused purely on prompt engineering or API-based LLM usage. If you have contributed to large-scale training pipelines, model optimization, or architecture-level improvements, we would like to hear from you.