
Remote
Security is only as strong as the systems behind it. Come build the backbone of ours.
At Resilient Privacy, the backend isn't a support act - it's where the real intelligence lives. KANSHI ingests threat data, runs analysis pipelines, and surfaces actionable alerts to security teams at MSPs across the country. You'll be in the engine room, writing systems that need to be correct, fast, and resilient under pressure. Sound familiar?
What You'll Be Building
Async services and pipelines in Python and Rust that handle real security event data
Database schemas and queries that perform - SQL design with an eye toward scale
APIs that our React frontend depends on - you'll feel it immediately if something breaks
Tests. Not as an afterthought. From the beginning. We believe in them.
Git-based workflows that mirror what professional engineering teams actually do
Skills We're Looking For
Strong grasp of async programming - you understand why it matters and when it bites back
Python fluency; Rust experience or genuine curiosity about it
SQL fundamentals: schema design, indexes, queries that don't make a DBA cry
Software testing principles - unit, integration, what to mock and what not to
Git as a tool for collaboration, not just saving files
Why This Internship Is Different
You'll work directly with the Founding CTO - not a junior engineer managing interns
Your code will touch production infrastructure for an active cybersecurity SaaS product
You'll learn what it means to build secure-by-default systems from engineers who live it
Exposure to threat intelligence pipelines, automotive cybersecurity contexts, and AI/ML integration
Flexible scope - if you're exceptional, we expand your responsibilities
Compensation & Commitment
Unpaid - we're a seed-stage startup and transparency matters to us
Equity conversation possible for interns who return or convert full-time
Letter of recommendation for strong performers - we'll write one worth reading
Flexible hours; fully remote with optional Dallas meetups
The best backend engineers learn by working on real systems under real constraints. This is that.