Senior Research Engineer

Job Description

Senior Research Engineer | FAR.AI Careers We updated our website and would love your feedback! Research Research Research Overview All Publications Deception Robustness & Security Interpretability Red-Teaming & Evaluation Alignment Events Events Events Overview Alignment Workshop FAR.AI Seminar Specialized Workshops All Recordings Programs Programs Programs Overview Programs Overview FAR.Labs Blog About About About About Us Meet the Team Contact Us Newsletter Careers Donate Careers / Senior Research Engineer Senior Research Engineer This role is filled. Apply Role overview Application tab 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. 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 40+ 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 to incubate other AI safety organizations, currently housing 40 members. 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 : F inding 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 co