Technical Lead

FAR AI is seeking applications for a skilled Technical Lead to spearhead the delivery of our technical AI safety research and red-teaming projects.

About Us

FAR AI is a technical AI research non-profit, focused on ensuring the safe development and deployment of frontier AI technologies.

Since starting in July 2022, FAR has grown to 12 FTE, produced 13 academic papers, hosted events for some of the world’s leading AI & computer science researchers, and opened our AI safety focused co-working space which is home to around 40 members.

About FAR Research

Our research team likes to move fast. We explore promising research directions in AI safety and scale up only those showing a high potential for impact. Unlike other AI safety labs that take a bet on a single research direction, FAR aims to pursue a diverse portfolio of projects. We also put our research into practice through red-teaming engagements with frontier AI developers.

Our current focus areas are building a science of robustness (e.g. finding vulnerabilities in superhuman Go AIs), finding more effective approaches to value alignment (e.g. training from language feedback), and model evaluation (e.g. inverse scaling and codebook features).

Other FAR Projects

To build a flourishing field of AI safety research, we host targeted workshops and events, and operate a co-working space in Berkeley, called FAR Labs. Our previous events include the International Dialogue for AI Safety that brought together prominent scientists (including 2 Turing Award winners) from around the globe, culminating in a public statement calling for global action on AI safety research and governance. We recently hosted the New Orleans Alignment Workshop for over 140 researchers from academia and industry to learn about AI safety and find collaborators. For more information on FAR AI’s activities, please visit our latest post.

About the Role

Our research projects are focussed on ensuring the safe and responsible development of advanced artificial intelligence systems. As a Technical Lead at FAR AI you will be crucial to driving the quality and execution of these projects to new heights, helping shaping the future of AI safety.

You will work in tandem with Research Scientists, Advisors and Engineers both within and outside FAR, providing technical guidance and project stewardship.

Key responsibilities:

  • Technical Leadership and Execution. Be FAR’s point-of-contact for all technical matters related to our research projects.
  • Strategic Guidance. Play a key role in the development and direction of technical research projects.
  • Hands-on Leadership. Lead by example with active involvement in coding, debugging and project development.
  • Innovation. Run scientific experiments and contribute to the development of scalable implementations of machine learning algorithms. You will be credited as an author in submissions to peer reviewed venues (e.g. NeurIPS, ICLR, JMLR)
  • Research Collaboration. Work closely with Research Scientists to convert theoretical models into practical experiments. Foster strong relationships with collaborators from various academic labs and research institutions, facilitating the exchange of ideas and methodologies.
  • Mentorship and Team Development. Guide and support the professional growth of the engineering team.
  • Learning and Development. Pursue continual development of your skills. You will have an opportunity in the role to develop your research taste and high-level views on AI safety research by collaborating with our Research Team. We are excited to support our team to grow in other areas, and will have a dedicated Learning & Development budget.

About You

You should be excited to help make AI systems safe. You are expected to have a minimum of 5 years experience in software development OR 3 years (PhD equivalent) experience with Machine Learning methodologies.

It is essential that you have a:

  • Track record of delivering technical projects, ideally in a startup or research environment
  • Strong technical expertise in software engineering, with proficiency in at least one object-oriented programming languages (preferably Python).
  • Are results-oriented and motivated by impactful research.
  • Excellent ability to communicate complex technical concepts and lead collaborative efforts.

It is preferable that you have:

  • Experience with machine learning methodologies and frameworks like PyTorch or TensorFlow.
  • Familiarity with operating system internals and distributed systems.
  • Familiarity with basic linear algebra, calculus, vector probability, and statistics.
  • Publications or open-source software contributions.

About the Projects

As a Technical Lead you would drive engineering excellence across multiple projects, with examples below:

  • Scaling laws for prompt injections. Will advances in capabilities from increasing model and data scale help resolve prompt injections or “jailbreaks” in language models, or is progress in average-case performance orthogonal to worst-case robustness?
  • Robustness of advanced AI systems. Explore adversarial training, architectural improvements and other changes to deep learning systems to improve their robustness. We are exploring this both in zero-sum board games and language models.
  • Mechanistic interpretability for mesa-optimization. Develop techniques to identify internal planning in models to effectively audit the “goals” of models in addition to their external behavior.
  • Redteaming of frontier models. Apply our research insights to test for vulnerabilities and limitations of frontier AI models prior to deployment.


You will be an employee of FAR AI, a 501(c)(3) research non-profit.

  • Location: Both remote and in-person (Berkeley, CA) are possible. We sponsor visas for in-person employees, and can also hire remotely in most countries.
  • Hours: Full-time (40 hours/week).
  • Compensation: $125,000-$250,000/year* depending on experience and location. We will also pay for work-related travel and equipment expenses. We offer catered lunch and dinner at our offices in Berkeley. *For exceptional candidates with an outstanding track record we may be able to offer additional compensation.
  • Application process: A 90-minute programming assessment, 2 1-hour interviews, 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.
  • Deadline: May 30th, 2024 – earlier applications preferred, we may close the round earlier if a suitable candidate is found.

Please apply! If you have any questions about the role, please do get in touch at