Platform Security Engineer

Brain Corporation
San Francisco, United States
2 days ago
Apply on startup.jobs
Prepare application

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Languages
English
Job source

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Amazon Web Services Audit Trail Automation of Tests Microsoft Azure Computer Programming Customer Data Management Data Security Python (Programming Language) Key Management Machine Learning
+12 more
Systems Development Life Cycle Role-Based Access Control TypeScript Enterprise Data Management Policy as Code Scripting Large Language Models Build Management Kubernetes Production Code Virtual Agents Golang

Job description

We’re looking for a Platform Security Engineer to build the guardrails that keep our AI agents safe to run on real customer data. This isn’t a policy or compliance role-it’s a builder role. You’ll design and ship the code that constrains what an agent can access, do, and expose: scoped credentials, data access boundaries, action validation, and audit trails, so product teams can put agents in front of sensitive government, healthcare, and enterprise data with confidence.

You’ll work as a software engineer embedded with our product and agent-platform teams-writing production code, not just policy-to make the secure path the only path an agent can take.

What You’ll Build & Ship

  • Design and build the guardrail services that mediate actions an AI agent takes, scoped permissions, tool-call validation, and hard limits on what an agent can read, write, or send
  • Write production code for data access controls that keep customer PII and sensitive records inside approved boundaries, even when an agent is orchestrating the request
  • Build reusable guardrail libraries and SDKs so product engineers can drop data protection and permissioning into new agent workflows without reinventing it each time
  • Design detection and containment for agent-specific failure modes, prompt injection, tool misuse, data exfiltration attempts, and build automated tests and red-team harnesses to catch them before production
  • Instrument agents with tamper-evident audit logs and decision trails so every customer-data access is explainable after the fact
  • Partner with product, platform and ML engineering to review new agent capabilities before launch and flag where guardrails are missing
  • Own the developer experience for guardrails: clear APIs, documentation, and low-friction integration so engineers adopt controls instead of routing around them
  • Help define and measure guardrail effectiveness, coverage across security workflows, false positive/negative rates, mean time to detect and contain

Requirements

  • 5 to 8 years as a software engineer building and shipping production systems, with meaningful time spent on security, data protection, or trust & safety problems
  • Strong general-purpose programming skills (Python, Go, TypeScript, or similar), comfortable designing services and APIs other engineers depend on, not just writing scripts or config
  • Experience with or strong working knowledge of how AI agents operate in production, tool use, function calling, orchestration frameworks (LangChain, LangGraph, or similar)
  • Solid grasp of data protection fundamentals: PII handling, access control, encryption, and least privilege, and how they hold up once an agent is in the loop
  • Comfortable designing systems used by other engineers-clear interfaces, sensible defaults, predictable failure modes
  • Working cloud experience (AWS, GCP, or Azure) sufficient to build and deploy services securely
  • Comfortable across the SDLC, understands how developers work and designs guardrails that don’t create friction
  • Strong written English; able to write documentation and runbooks engineers actually read, * Direct experience building guardrails or safety layers for LLM/agent systems-prompt injection defenses, content filtering, output validation
  • Background in regulated industries (healthcare, government, financial services) handling sensitive customer data
  • Familiarity with policy-as-code, secrets management, or software supply-chain security tooling
  • Prior startup experience; comfort with ambiguity and working autonomously

About the company

Brain Co. is entering its next phase of production deployments on a national scale with an elite team built from Palantir, Google, Meta, and Nvidia, and a growing footprint across government, insurance, health, and financial services.

Joining now means shaping both the company and a new category of applied AI. Every project here ships to production and is expected to create measurable customer value and impact.

You’ll work alongside exceptional peers on some of the hardest problems in applied AI. It’s the kind of work you’ll still be proud of in ten years from now.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on startup.jobs
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

3:22 min

Transitioning from software engineering to security roles

Anna Oliveira · Coffee With Developers

1:08 min

Building solutions with open source GoLang infrastructure tools

Jad Wahab · LIVE

1:04 min

Introduction to Bitcoin script parsing tools

Steve Shadders · LIVE

2:28 min

Understanding Kubernetes architecture and core cluster components

Marc Nimmerrichter · World Congress 2022

1:45 min

Transitioning from software development to security roles

Stefania Chaplin · World Congress 2022

6:16 min

Event-driven Golang backend architecture and cloud deployment

Irina Branovic Irina Branovic · World Congress 2026 Europe

Videos

See all

Related articles

See all