SDE III - Engineering Productivity (AI)

India·BangaloreFull-timemid

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Quick Summary

Requirements Summary

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,

Technical Tools

OtherSde

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.

What We Offer

~5 min read

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.