About Us

FAR.AI is a non-profit AI research institute working to ensure advanced AI is safe and beneficial for everyone. Our mission is to facilitate breakthrough AI safety research, advance global understanding of AI risks and solutions, and foster a coordinated global response.

Since our founding in July 2022, we've grown to 50+ staff, published 40+ academic papers, and convened leading AI safety events. Our work is recognized globally, with publications at premier venues such as NeurIPS, ICML including a Best Paper Honorable Mention in 2026, and ICLR, and features in the Financial Times, Nature News, Wired Magazine and MIT Technology Review. We conduct pre-deployment testing on behalf of frontier developers such as OpenAI, independent evaluations for governments including the EU AI Office, and publish the AI Security Leaderboard based on our red-teaming expertise. We help steer and grow the AI safety field through developing research roadmaps with renowned researchers such as Yoshua Bengio; running FAR.Labs, an AI safety-focused co-working space in Berkeley housing 40+ members; and supporting the community through targeted grants to technical researchers.

About FAR.Research

We explore promising research directions in AI safety and scale up only those showing a high potential for impact. Once the core research problems are solved, we work to scale them to a minimum viable prototype, demonstrating their validity to AI companies and governments to drive adoption.

We are aiming to rapidly grow our team in the following areas:

Mitigating AI deception: Studying when lie detectors induce honesty or evasion, and developing for deception and sandbagging

Evals and red-teaming: Conducting pre- and post-release adversarial evaluations of frontier models (e.g. Claude 4 Opus, ChatGPT Agent, GPT-5); developing novel attacks to support this work; and exploring new threat models (e.g. persuasion, tampering risks).

Infrastructure: Maintaining GPU compute infrastructure to support experiments with open-weight models and developing new tooling to allow our research teams to scale their fine-tuning and post-training workflows to frontier open-weight models.

Adversarial Robustness: Working to rigorously solve these security problems through building a science of security and robustness for AI, from demonstrating superhuman systems can be vulnerable, to scaling laws for robustness and jailbreaking constitutional classifiers

Mechanistic Interpretability: Finding issues with Sparse Autoencoders, probing deception using AmongUs, understanding learned planning in SokoBan and interpretable data attribution.

FAR.AI is one of the largest independent AI safety research institutes, and is rapidly growing with the goal of diversifying and deepening our research portfolio. We would welcome the opportunity to add new research directions if you are a senior researcher with a strong vision and would like to pitch us on it.

About the Role

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)