Senior Research Engineer

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)
• 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.

Logistics

If based in the USA or Singapore, you will be an employee of FAR.AI (501(c)(3) research non-profit / non-profit CLG). Outside the USA or Singapore, you will be employed via an EOR organisation on behalf of FAR.AI or as a contractor.

• Location: Both remote and in-person (Berkeley, CA or Singapore) are possible. We sponsor visas for in-person employees, and can hire remotely in most countries.
• Hours: Full-time (40 hours/week).
• Catered Meals (Berkeley Office Only): Catered lunches and dinners on workdays at our office
• Application process: A 72-minute programming assessment, two interviews with members of our technical staff, and a 1-2 week paid work trial. If you are not available for a work trial we may be able to find alternative ways of testing your fit.

If you have any questions about the role, feel free to contact us at. Otherwise, if you don't have questions, the best way to ensure a proper review of your skills and qualifications is by applying directly via the application form. Please don't email us to share your resume (it won't have any impact on our decision). Thank you!