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.
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.
We want our team to feel like owners because they are owners. We trust our people to manage their results and their time.
Meaningful Equity: Every "Safestar" is a shareholder. You aren’t just an employee; you are a partner in our success.
Unlimited Leaves: We don’t believe in clock-watching. We offer unlimited leave because we trust you to take the time you need to recharge while staying committed to the mission.
Comprehensive Benefits: We provide top-tier medical insurance and wellness benefits to ensure you and your family are well cared for.
Career Trajectory: We are growing aggressively. For high-performers, the path for advancement moves at the speed of your ambition.
Be the Tech Leader: Mentor engineers, champion clean code, and drive a culture of technical excellence.
Architect at Scale: Design secure, high-performance multi-tenant microservices that can handle serious load—without breaking a sweat.
Code Like a Pro: Conduct deep-dive code reviews and stay hands-on when it matters most.
Master the Cloud: Architect smart solutions using AWS (Lambda, API Gateway, EC2, S3, RDS—you name it).
Solve Complex Problems: Debug, diagnose, and dismantle architectural challenges with finesse.
Collaborate & Influence: Work closely with Product, Design, and GTM teams to align tech with business.
Drive Delivery: Own the roadmap, lead execution, and ensure on-time, high-quality releases.
Innovate Constantly: Introduce and champion new tools, patterns, and practices that elevate our engineering game.
8+ years of experience as a Staff/Senior Engineer or equivalent.
Strong backend chops in Node.js, Python, Go, or similar.
Hands-on experience building APIs in TypeScript (Express is a plus).
Bonus points if you’ve played with modern frontends (React, Angular, Vue).
Deep database expertise (SQL & NoSQL), with performance tuning skills.
Proven success building on the cloud (AWS/GCP/Azure).
Containerization know-how—Kubernetes or Docker is your playground.
You’ve built and scaled platforms that handle real-world load.
Strong leadership, mentoring, and cross-functional collaboration skills.
You love tackling tough architecture challenges head-on.
Familiarity with DevOps and IaC (CloudFormation is a plus).
Strong working knowledge of Generative AI and LLM fundamentals, including how LLMs work, tokens, context windows, prompting, hallucinations, embeddings, and common model limitations.
Hands-on experience using AI-assisted development tools such as Cursor, Claude Code, GitHub Copilot, or equivalent, with the ability to critically review and validate AI-generated code, tests, and technical solutions.
Understanding of common LLM application patterns, including structured outputs, tool/function calling, retrieval-augmented generation (RAG), and agentic workflows.
Ability to make informed engineering decisions about when to use deterministic software, a direct LLM call, retrieval/RAG, or an agentic approach, based on the problem being solved.
Ability to define how AI-powered capabilities should be evaluated, including the use of golden datasets, appropriate accuracy metrics, regression testing, human evaluation, and LLM-as-a-judge where appropriate.
Ability to reason about production AI trade-offs, including accuracy, latency, cost, reliability, security, and observability.