This role would be a good fit for an experienced machine learning engineer, or an experienced software engineer looking to transition to AI safety research. All candidates are expected to: Have significant software engineering experience. Evidence of this may include prior work experience and open-source contributions. Be fluent working in Python. Be results-oriented and motivated by impactful research. Bring prior experience mentoring other engineers or scientists in engineering skills. Additionally, candidates are expected to bring expertise in one of the following areas corresponding to the core competencies our different research teams most need: Option 1 – Machine Learning: Substantial experience training transformers with common ML frameworks like PyTorch or jax. Good knowledge of basic linear algebra, calculus, vector probability, and statistics. Option 2 – High-Performance Computing: Power user of cluster orchestrators such as Kubernetes (preferred) or SLURM Experience building high-performance distributed-systems (e.g. multi-node training, large-scale numerical computation) Experience optimizing and profiling code (ideally including on GPU, e.g. CUDA kernels). Option 3 – Technical Leadership: Experience designing large-scale software systems, whether as an architect in greenfield software development or leading a major refactor. Comfortable project managing small teams, such as chairing stand-ups and developing detailed roadmaps to execute on a 3-6 month research vision.