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
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