Senior Research Engineer

About Us

FAR.AI is a non-profit AI research institute dedicated to ensuring 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. Founded in July 2022, we have grown quickly to 30+ staff. We are uniquely positioned to conduct technical research at a scale surpassing academia and leveraging the research freedom of being a non-profit.

Our work is published at top conferences (e.g. NeurIPS, ICLR, ICML) and cited by leading media outlets such as the Financial Times, Nature News and MIT Technology Review.

FAR.AI uses three prongs working together to improve AI safety:

• FAR.Research - we conduct cutting-edge AI safety research in-house and dispense grants to support the wider research community.
• FAR.Futures - we bring together key policy makers, researchers and companies to drive change, such as the San Diego Alignment Workshop or the Guaranteed Safe AI research roadmap written with Yoshua Bengio.
• FAR.Labs - we host a co-working space in Berkeley to help incubate other AI safety organizations, currently housing 40 members.

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 jail breaking constitutional classifiers.
• Mechanistic Interpretability
  :
  Finding issues with Sparse Autoencoders, probing deception using Among Us, understanding learned planning in Soko Ban 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.

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:
• Expe…