Machine Learning Research Engineer - LLM post-training/mid-training Our team is partnered with a materials discovery stealth venture based in San Francisco and London, led by former Oxford and Isomorphic Labs leaders in AI and experimental science. The team is pioneering large-scale language models that reason, adapt, and accelerate discovery workflows. They are combining experimental validation, synthetic data generation, and scalable infrastructure to push the boundaries of autonomous research. This is a rare chance to be part of the founding team, shaping technical direction and building systems that redefine how science is done. The Role You will design and implement novel approaches for model adaptation and reasoning, exploring techniques that improve generalization, controllability, and scientific understanding. This includes mid-training strategies, post-training alignment, and inference-time optimization for complex workflows. You'll also develop new reasoning paradigms such as retrieval-augmented and tool-augmented approaches and build robust evaluation frameworks for applied scientific contexts. Qualifications: PhD. or equivalent experience in Computer Science, Machine Learning, or a closely related discipline Practical experience working with LLM training pipelines, including pre-training, mid-training, or post-training stages Strong grasp of transformer architectures, optimization techniques, and representation learning principles Proficiency in Python and familiarity with major ML frameworks such as PyTorch, DeepSpeed, or JAX Knowledge of alignment and reasoning strategies, including in-context learning, chain-of-thought, tool integration, or retrieval-augmented approaches Ability to combine innovative research thinking with pragmatic engineering to deliver scalable, high-performance systems