Brain Co.
Tech / AI / Software
AIPlatformEngineer,Backend(AgenticEngineering)
Neural analysis suggests this role is
optimal for Senior candidates.
“AI Platform Engineer, Backend (Agentic Engineering) at Brain Co.. Skills: backend systems, distributed systems, APIs, services, shared infrastructure, internal platforms, cloud-native, LLM infrastructure, agent frameworks. Own the foundations of how LLMs are used across the company: cost visibility and controls, data privacy, identity and access, routing, and the security posture around all provider traffic. Design the sandboxing, orchestration, audit, and guardrail layers that product teams bui”
What You'll Achieve.
cost visibility and controls; data privacy; identity and access; routing; security posture; sandboxing; orchestration; audit; guardrail layers; prompt-injection defenses; scoped credentials; kill switches; multi-tenant isolation; runaway-cost controls; resource models; credential and token lifecycle; fan-out and fan-in patterns; fairness and quota enforcement; observability; AI-assisted development; coding agents that review and ship code; automate CI; refactor at scale; run as background workers; run their own agents reliably and safely; right credentials; scheduling; memory; audit underneath; agent deployments safe by default
Industry & Context.
Solve the hard problems: prompt-injection defenses, scoped credentials, kill switches, multi-tenant isolation (including VM-level pod isolation), and runaway-cost controls; fundamentals in distributed systems: consistency, idempotency, retries, failure modes, queueing, scheduling
on-call
What They're Looking For.
Must Have
5+ years building backend systems in production, deep proficiency in at least one of Python, TypeScript, Go, or Rust, fundamentals in distributed systems: consistency, idempotency, retries, failure modes, queueing, scheduling, designed and operated APIs and services that other engineers depend on, proven track record building shared infrastructure, internal platforms, or developer-facing services that real users adopted, intuition for developer experience, long-term maintainability, and where to draw abstraction boundaries, comfortable owning the full lifecycle: writing the design doc, shipping the MVP, hardening it, and driving adoption across the company, owned services with real uptime and operational responsibility, comfortable with observability stacks, incident response, and SLOs, cloud-native experience: Kubernetes, infrastructure-as-code, OAuth/OIDC, secrets management
Nice to Have
Experience building or operating LLM infrastructure: gateways, inference systems, prompt routing, cost attribution, evaluation harnesses, Experience with agent frameworks, tool-use systems, or sandboxed code execution, Security instincts around prompt injection, supply-chain risk in agent ecosystems, and credential scoping for autonomous systems, Background in multi-tenant, regulated, or government deployments (HIPAA, SOC2), Open-source contributions to AI infrastructure, agent tooling, or developer platforms
What You'll Do.
Own the foundations of how LLMs are used across the company: cost visibility and controls
and the security posture around all provider traffic
Design the sandboxing
and guardrail layers that product teams build their agents on
Solve the hard problems: prompt-injection defenses
multi-tenant isolation (including VM-level pod isolation)
and runaway-cost controls
Design the orchestration
and resource models that make this viable: cold-start vs. always-on tradeoffs
credential and token lifecycle
fan-out and fan-in patterns
fairness and quota enforcement across tenants
and the observability needed to debug at that volume
Make AI-assisted development a first-class platform layer: coding agents that review and ship code
and run as background workers across the codebase
together with the canonical scaffolding and guardrails that govern them
Build the systems that let every engineering
run their own agents reliably and safely against the tools they already use
with the right credentials
End-to-end ownership: architecture
and iteration based on internal user feedback
How You'll Work.
Team & Collaboration
Partner closely with security, infrastructure, and product teams to make agent deployments safe by default
Process & Methodology
writing the design doc, shipping the MVP, hardening it, driving adoption
Full Job Description
ABOUT BRAIN CO. Brain Co. is an applied AI startup co-founded by Jared Kushner and Elad Gil, and backed by leading Silicon Valley builders including Patrick Collison and Andrej Karpathy. We are building AI applications for the world’s most important institutions, delivering impact on real-world problems across governments, healthcare systems, and critical industries. Our progress so far: - Automated construction permitting for a sovereign government → 80% faster, unlocking $375M+ in value - Optimized supply chains for a leading global energy company → 30% lower cost, 99% reliability, preventing $100M+ in losses - Streamlined hospital patient care across national health systems → 40% better outcomes, 80% less admin work Company momentum: - Raised a $55M Series A from leading investors - Built a team of 70+ AI experts from Tesla, Google DeepMind, NVIDIA, and Databricks At Brain Co., we focus on applying frontier AI to real institutional challenges, working alongside governments, healthcare systems, and critical industries to modernize how essential services operate. We are looking for leaders who want to help bring new technology into institutions that impact millions of people. ABOUT THE ROLE: You'll join the team that builds and enables agentic workflows across Brain Co. For every engineer, operator, and business team internally, and for the production AI systems we deploy to governments, healthcare systems, and critical industries. This is a platform role at the center of the company's agent-first strategy: you'll build foundational systems used by every engineering team, and the bar is product-grade because the entire company depends on them. WHAT YOU’LL WORK ON: - Own the foundations of how LLMs are used across the company: cost visibility and controls, data privacy, identity and access, routing, and the security posture around all provider traffic. - Design the sandboxing, orchestration, audit, and guardrail layers that product teams build their agents on, so ve
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