Head of Engineering (Frontier Safety)

FAR.AI is seeking a Head of Engineering for its Frontier Safety division, which conducts testing and evaluations of frontier AI systems. The role will build and lead the division’s engineering team and systems, scaling tools, infrastructure, and execution for red-teaming, safeguards, research, and high-stakes work with AI companies and governments.

Responsibilities:

• Build and scale red-teaming tools and products that improve speed, coverage, and importance of findings
• Operationalize and scale systems that advance state-of-the-art attacker simulation and output severity evaluation
• Maintain and evolve internal vulnerability databases, statistical analysis tools, and reporting infrastructure
• Develop agentic systems, including automated red-teaming agents that systematically explore the attack space, agents that identify and automatically integrate new public jailbreaks, and agents that automate workflows to accelerate the division broadly
• Build, test, and refine sandboxing architecture that enables rapid and thorough evaluation of the strongest AI systems, while ensuring strong security appropriate for high-stakes risk domains like cybersecurity and autonomy
• Extend our infrastructure and tooling to support the division's CBRN and cyber evaluations and loss-of-control testing
• Design and implement architecture that enables external contributions to our platform and integration with the AI evaluation ecosystem (e.g., the Inspect framework), while being privacy compliant and minimizing vulnerability exposure
• Integrate the systems your team builds with the overall division to empower both velocity and quality of testing, leading to high impact private disclosures; public reports, benchmarks, and leaderboards that shape industry norms; and feedback loops that continuously turn insights from real-world outcomes into advances in our overall systems
• Mentor team members in engineering skills, both in direct 1-1 settings and by building resources for rapid skill growth and knowledge transfer
• Manage team members, supporting and amplifying their impact, setting high standards and prioritization week-to-week, and creating the environment and paths for rapid growth
• Initially build and manage a team of ICs (partly from a small number of existing engineering-focused team members, partly from hiring), then hire or grow managers over time according to engineering and division needs
• Design and be hiring manager for pipelines to scale the engineering team, covering ICs, engineering managers, and other roles. Work in partnership with others in the division and the org’s recruiting team to deliver on these pipelines
• Contribute to the overall technical and product strategy of the division, proactively identifying, prioritizing, and solving engineering bottlenecks
• Pair closely with Kellin on technical direction, particularly where engineering systems, red-teaming methodology, evaluations, safeguards, and research intersect; take broad technical goals and turn them into scalable engineering plans, teams, and systems
• Work closely with and potentially manage the company engagements and testing team, deepening feedback loops between the systems you build that are used in testing, and the testing that develops new ways to improve the systems
• Partner closely with the rest of the division and with teams across FAR.AI to continuously translate technical ideas and findings into real-world impact, including building testbeds and tooling that other FAR.AI teams can use in their research

Requirements:

• Have a strong track record in software engineering, AI, or another computer engineering discipline (e.g. cybersecurity, MLOps)
• Have a strong track record of managing, growing, and leading technical teams, including both building start-ups (0 → 1) and scale ups (managing managers)
• Have experience with parts of the tech stack we commonly use. For example, coding agents (e.g., Claude Code, Codex, Cursor), Python, LLM APIs (e.g. OpenAI, Anthropic, Google), local LLM frameworks (e.g., vLLM), evaluation frameworks (e.g., Inspect), LLM agents (e.g., OpenClaw), cloud infrastructure (e.g. GCP), compute clusters (e.g. Kubernetes, Slurm), etc
• Have thrived in rapidly evolving environments
• Demonstrated drive for mission/impact and desire to create a real impact on frontier AI systems
• Can effectively communicate technical solutions to both technical and non-technical audiences
• Demonstrated relentlessness in achieving ambitious goals
• Experience building, red-teaming, or evaluating frontier LLMs or agentic systems, or building safeguards for them
• Experience building technical teams and products in an entrepreneurial environment
• Demonstrated ability to discover non-obvious, high-severity vulnerabilities in complex systems
• Other experience in closely related areas like adversarial ML or security
• Prior collaboration with AI labs, security teams, or government safety institutes
• Published work in AI safety, security, or robustness