AI & Security Engineering

Member of Technical Staff, AI & Security Systems

Own foundational AI and security systems for autonomous agents that find, prove, and help remediate real vulnerabilities.

Location: San Francisco Bay Area or Singapore
Employment: Full-time
Industry: Cybersecurity

FailSafe is building SWARM, an agentic offensive security platform that continuously tests applications, APIs, and infrastructure, validates exploitability, and helps security teams separate meaningful risk from noise.

Member of Technical Staff is a senior individual contributor role for engineers who want broad technical influence without moving away from hands-on work. You will own foundational domains across SWARM's agent runtime, orchestration, evidence, evaluation, and production infrastructure, carrying ideas from first principles through deployment and operation.

You will work with substantial autonomy, make decisions that shape the platform, lead complex technical investigations, and raise the engineering standard around you. This is a full-time role based in the San Francisco Bay Area or Singapore.

The opportunity

Why this role

Take architectural ownership of a young, technically ambitious platform where the most important systems and abstractions are still being defined.

Work across AI, distributed systems, and offensive security on problems where correctness and evidence matter as much as model capability.

Stay deeply hands-on while shaping technical direction, mentoring strong engineers, and influencing what FailSafe becomes.

What You’ll Do

Own one or more critical technical domains across SWARM's agent runtime, orchestration, evidence, evaluation, or production infrastructure, with accountability from architecture through reliability.
Design and build high-performance backend, distributed, and AI systems that operate reliably against real customer attack surfaces.
Set the architecture for orchestration and adjudication systems that reconcile multi-model outputs, resolve conflicting findings, reduce false positives, and validate exploit paths.
Move ambiguous concepts from research or customer requirements into clear technical plans, production code, measurable evaluations, and durable platform capabilities.
Diagnose critical failures across models, data, infrastructure, and application code, and lead cross-functional investigations through root cause and remediation.
Make consequential trade-offs around system design, model quality, performance, security, cost, and delivery speed.
Partner closely with security researchers, applied AI engineers, product leaders, and customer-facing teams to set technical direction and execution priorities.
Establish strong engineering practices for testing, observability, code review, incident response, performance, and reliability.
Mentor engineers, strengthen architectural judgment across the team, and raise the quality of technical decisions without relying on formal authority.
Remain deeply hands-on: write and review production code, build prototypes, debug difficult systems, and step into the most consequential technical work.

Qualifications

Typically eight or more years of software engineering experience, or an equivalent record of designing and shipping complex technical systems with increasing scope and impact.
Demonstrated ownership of a substantial platform, product area, or technical domain from design through production operation.
Expert-level ability in at least one systems or application language such as Python, Go, Rust, C++, Java, or TypeScript, with the range to work across an unfamiliar stack when needed.
Deep experience with distributed systems, cloud infrastructure, backend architecture, data systems, or production AI/ML platforms.
Strong understanding of modern AI systems, including model serving, agent orchestration, evaluation, data pipelines, reliability, and failure analysis.
Ability to debug complex cross-layer failures and make sound architectural decisions under uncertainty and operational pressure.
Applied security judgment across web applications, APIs, cloud infrastructure, secure system design, or offensive security concepts.
A record of mentoring engineers, leading technical reviews, and improving team-wide engineering and architectural standards.
Clear communication with technical and non-technical partners, low-ego collaboration, and the autonomy to drive difficult work with minimal oversight.
Bachelor's degree or higher in computer science, engineering, machine learning, or a related field, or equivalent practical experience.

You’ll Be Especially Strong If You Have

Architected orchestration or adjudication systems that combine multiple model or agent outputs in a security-sensitive environment.
Led a major system through rapid growth, a critical reliability challenge, or a significant architectural transition.
Taken AI capabilities from research prototype through deployment, evaluation, monitoring, iteration, and long-term production ownership.
Hands-on offensive security experience or deep collaboration with penetration testers, red teamers, and security researchers.
Built security-focused datasets, benchmarks, reward models, evaluation harnesses, or evidence systems.
A strong point of view on agent reliability, false-positive reduction, proof of exploitability, and evidence-based evaluation.

Success Looks Like

You become the trusted technical owner for a foundational part of the FailSafe platform.
The systems you lead are simpler to reason about, more reliable in production, and able to support greater scale and complexity.
Difficult technical decisions become clear, documented, and executable because of your judgment and leadership.
Engineers around you make stronger architectural choices and take on greater scope through your mentorship.
Research advances and customer needs consistently become durable product capabilities instead of isolated prototypes or one-off fixes.

Set the technical standard for SWARM.

Lead hard technical work from first principles to production, and build systems that help defenders act on evidence rather than noise.