Senior Research Engineer

FAR.AI is seeking a Senior Research Engineer to accelerate and scale-up our research. Your focus will be on tackling challenging engineering problems in one of our core safety agendas, mentoring and unblocking other staff members in their technical work, and increasing the depth and scale of our research work overall. 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. As a Member of Technical Staff (Senior Research Engineer) you would join one of our existing workstreams and lead projects there: 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. As we continue to grow our research portfolio, additional workstreams may open up for contribution, for example in mechanistic interpretability.

This role creates positive change by advancing AI safety research to prevent AI deception and misuse, contributing to safer AI development.

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.

No benefits information provided in the description.

FAR AI is an organization focused on AI safety research, working to address challenges like AI deception and misuse through technical solutions.