SDE III - Engineering Productivity (AI)

Most boards and executives are currently flying blind when it comes to cyber risk. They are guessing. At Safe, we’ve built an AI-driven engine that finally gives the C-Suite a clear, quantified, and real-time view of their security posture. We don’t just provide data; we provide certainty.

We are a $170M Series C-funded category leader. We don’t play in the mid-market; we operate at the highest levels of global enterprise. Today, we are proud to serve 10% of the Fortune 500, protecting global icons such as Apple, Netflix, AT&T, Verizon, and Victoria’s Secret.

As we scale toward our next chapter, we are looking for high-performers who want to do the best work of their careers at the intersection of AI and Cybersecurity.

The Culture Memo: Our Operating System

Safe is not a typical corporate environment. We are a high-intensity, mission-driven team. We value builders who want to define a category and work alongside people who are equally committed to excellence.

• Extreme Ownership: We don’t do "not my job." We hire people who see a gap and own the solution from start to finish.

• The Elite Standard: We serve the most sophisticated companies on the planet. Our work must be bulletproof. Whether it’s a line of code or a sales deck, we aim for Tier-1 quality every time.

• Methodology & Rigor: We don’t wing it. From Force Management and MEDDICC in sales to data-driven sprints in engineering, we rely on proven frameworks to stay disciplined and predictable.

• Radical Candor: We move too fast for politics or sugar-coating. We value direct, honest feedback that helps us find the right answer quickly.

• The Series C Hustle: We have the stability of a well-funded leader but the heart of a startup.

The Perks & Ownership:

This role exists to change how engineering at Safe gets work done — not to add AI tools on top of today's workflow, but to rebuild the engineering operating model around AI, automation, and redesigned process, and to prove the gain in numbers.

You will look across the full lifecycle — requirements, design, implementation, review, testing, deployment, operations, incidents, documentation — find where engineers lose time, and decide what actually fixes it: an AI agent, an automation, a platform capability, or a process change. Then build it to production quality, drive adoption, and measure whether cycle time moved.

This is not a Developer Experience, DevOps, or internal-tools role. It is a software engineering role whose product is engineering throughput.