Backend / AI Infrastructure / Platform Engineer

About AIDAChip

AIDAChip is the Alignment OS for AI-native silicon engineering teams — the first AI platform built for chip design teams, not just individuals. Chip design today is fragmented across disciplines, tools, and handoffs, so intent gets lost and work drifts. We're the shared nervous system that keeps intent, knowledge, and execution aligned from specification to tapeout. AIDAChip is the Alignment OS for AI-native silicon engineering teams — the first AI platform built for chip design teams, not just individuals. Chip design today is fragmented across disciplines, tools, and handoffs, so intent gets lost and work drifts. We're the shared nervous system that keeps intent, knowledge, and execution aligned from specification to tapeout.

The Role

The multiplayer platform only works if the system underneath it is airtight. That's what you defend. You'll own security for the AI infrastructure that a fleet of AI teammates (and more) runs on: the agent runtime, orchestration, APIs, and data model. This is event-driven, multi-agent orchestration of long-running work that a design team trusts with a million-dollar tapeout, where a single leaked key or crossed tenant boundary can burn that trust. You'll ship product features across the stack with security as a first-class design input, working closely with our backend lead to raise our shipping velocity without lowering the reliability or security bar as we scale from alpha to real enterprise usage.

What you'll do

• Build product features as a full-stack-capable backend engineer, with security as a first-class design input
• Secure our multi-agent runtime: agent/tenant isolation, tool-use guardrails, human-approval gating, and defenses against prompt injection and unsafe tool actions
• Harden how we integrate third-party LLM providers (OpenAI, Anthropic, etc.) - key handling, data-egress controls, provider-side data boundaries
• Own secure SDLC: threat modeling, secure code review, SAST/dependency scanning, and safe use of AI coding tools in our own pipeline
• Harden cloud-native infra (Kubernetes): network policy, IAM, secrets, workload isolation, supply-chain integrity
• Extend this posture to on-prem customer deployments, where we ship into environments we don't control

What we're looking for

• A security-first mindset: you threat-model by default and reason about how systems fail and who is impacted
• Strong production backend engineering and comfortable across a modern backend languages and databases
• Hands-on securing SaaS products
• Experience running distributed, cloud-native systems on Kubernetes
• Practical use of AI tools in the SDLC

Ways to stand out (tell us if you do)

• Hands-on with third-party LLM providers (OpenAI, Anthropic, etc.) via API/SDK
• Experience deploying and securing both on-prem and cloud environments
• Security certs, CTF, or prior security-eng title (we care about the skill, not the label)
• Experience with observability tooling and agent/LLM-specific security

How we're different

• Evidence over vibes: nothing is "done" until it's proven — results carry their proof, claims are verified before they're asserted, and failures surface loudly instead of slipping through
• Measured, not narrated: if it isn't measured, it didn't happen — we decide on data in a shared system of record, not on how good the update sounded
• Agent leverage: every person directs a fleet of AI teammates and delivers at a multiple of what a lone expert could, without lowering the bar
• Human-in-the-loop by design: the AI proposes; a human owns the calls that matter — judgment stays with the expert
• Greenfield ownership: you own a whole problem end to end — a real workstream on a production platform serving enterprise silicon customers, not a demo

Compensation

Competitive salary • Plus Equity