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Bengaluru, Karnataka, India
Hybrid
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.
ind the bottlenecks: Instrument how engineering time is actually spent — cycle time, review latency, CI failures, test authoring, operational toil, onboarding — and rank problems by recoverable hours.
Pick the right instrument: Decide per bottleneck whether the answer is AI, automation, conventional software, or process change. Reject AI where it's the wrong tool.
Apply AI across the lifecycle: AI-assisted development and refactoring, first-pass code review, test generation and suite optimization, automated documentation and release notes, design analysis, automated migrations, AI-assisted debugging, incident investigation, and root-cause analysis.
Build engineering agents: Agents that diagnose CI failures, investigate production issues, write tests, run dependency upgrades, propose security fixes, analyze PRs, and execute repetitive migrations — human-in-the-loop by default, autonomy earned per workflow.
Redesign the process: Rework code review, testing, CI, and incident management for a world where AI does the first pass — and define what stays human-owned and where approval is mandatory.
Build the platform: AI gateway, agent runtime and workflow orchestration, codebase intelligence, engineering context retrieval, and deep Git/CI/observability/ticketing integrations — plus APIs and SDKs so teams build their own AI workflows on it.
Solve the context problem: Give agents permission-aware access to code, architecture docs, service ownership, deployment state, telemetry, incidents, and standards — and own retrieval quality and freshness.
Set the guardrails: Quality, security, privacy, access control, approval gates, auditability, and evaluation for AI-generated change.
Prove the impact and drive adoption: Baseline, experiment, publish results, kill what doesn't move the metric, and mentor teams into the patterns that work.
6+ years building production software, with SDE3-level ownership of systems in production.
Strong Python, Java, Go, or equivalent — you ship production services and review others' code.
Solid distributed systems, API and microservice, and software architecture grounding.
Hands-on cloud infrastructure and CI/CD experience.
Demonstrated developer tooling and automation work that other engineers actually used.
Practical experience with LLMs and agentic systems in real systems, not only experiments.
Ability to reason quantitatively about workflows: baseline, hypothesis, experiment, measured result.
Strong writing and the ability to influence engineering teams without authority.
In 90 days: Engineering time-spend is baselined, top bottlenecks are quantified in recoverable hours, and the first automation is shipped and in use.
AI does the first pass: Code review, test generation, and CI failure diagnosis run through AI workflows by default; humans review judgment, not mechanics.
Cycle time drops measurably quarter over quarter, attributable to specific changes.
Agents carry real load across defined workflows, with approval gates, audit trails, and tracked reliability — while escaped defects and security findings do not rise.
