About the job
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, be fluent working in Python, be results-oriented and motivated by impactful research, and bring prior experience mentoring other engineers or scientists in engineering skills.
Responsibilities
- Detecting and preventing deception. Under what conditions can we reliably detect deceptive behaviour from models, and can such behaviour be effectively mitigated at scale? This would focus on large-scale training of transformers.
- Preventing catastrophic misuse. Apply our research insights to detect and mitigate vulnerabilities and other risks in frontier AI models. This would focus more on technical leadership
- Accelerating our research. Build frameworks and infrastructure that allows us to ask bigger questions and more rapidly run new experiments, to deepen our research. This would focus more on high-performance computing.
Qualifications
Minimum
- 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.
Preferred
- Substantial experience training transformers with common ML frameworks like PyTorch or jax.
- Good knowledge of basic linear algebra, calculus, vector probability, and statistics.
- 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).
- 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.