Prime Intellect is building the open superintelligence stack - from frontier agentic models to the infra that enables anyone to create, train, and deploy them. We aggregate and orchestrate global compute into a single control plane and pair it with the full RL post-training stack: environments, secure sandboxes, verifiable evals, and our async RL trainer. We enable researchers, startups and enterprises to run end-to-end reinforcement learning at frontier scale, adapting models to real tools, workflows, and deployment contexts.
We recently raised $15mm in funding (total of $20mm raised) led by Founders Fund, with participation from Menlo Ventures and prominent angels including Andrej Karpathy (Eureka AI, Tesla, OpenAI), Tri Dao (Chief Scientific Officer of Together AI), Dylan Patel (SemiAnalysis), Clem Delangue (Huggingface), Emad Mostaque (Stability AI) and many others.
Security is the single highest-stakes function at Prime Intellect. Our customers — from frontier AI labs to enterprises — trust us with their most valuable assets: proprietary models, training data, and the compute that powers them. This role owns the security posture of everything we ship: the hosted RL training platform, distributed GPU infrastructure, liquid compute marketplace, and all customer-facing surfaces.
You'll be the first dedicated security hire and will define how we think about security as a company — from threat modeling and secure architecture to incident response and compliance. You'll work directly with engineering, research, and leadership to embed security into every layer of the stack, and you'll manage relationships with external penetration testers and security auditors to continuously validate our defenses.
Own threat modeling across our entire surface area: multi-tenant training infrastructure, sandboxed execution environments, API surfaces, and internal tooling
Design and implement zero-trust networking, identity, and access control across distributed GPU clusters and cloud infrastructure
Build secure-by-default patterns for our platform engineers — auth, secrets management, supply chain integrity, container hardening
Architect tenant isolation and data boundary enforcement for hosted RL training workloads (customers run arbitrary code in our environments)
Develop security frameworks specific to AI infrastructure: model weight protection, training data isolation, checkpoint integrity, gradient privacy
Secure the RL training loop end-to-end — from environment execution in sandboxes to reward signal verification and model artifact storage
Build detection and prevention for AI-specific attack vectors: prompt injection across agentic pipelines, model exfiltration, adversarial environment manipulation
Scope, manage, and run point on external penetration tests across our platform, hosted training infrastructure, and liquid compute layer
Build and maintain an internal red-teaming practice — automated and manual — targeting our most critical systems
Drive vulnerability management: triage, remediation SLAs, and root cause analysis
Build security monitoring and alerting across infrastructure (distributed clusters, Kubernetes, cloud) and application layers
Implement runtime security for containerized training workloads and sandboxed environments
Own incident response — build the playbooks, run the drills, lead the post-mortems
Design audit logging and forensic capability across all customer-facing systems
Drive SOC 2 Type II readiness and other compliance frameworks required by enterprise customers
Own the security narrative for customer-facing materials — questionnaires, architecture reviews, trust documentation
Partner with GTM to unblock enterprise deals that depend on security posture
5+ years in security engineering, infrastructure security, or offensive security roles — ideally at companies operating multi-tenant cloud or compute infrastructure
Deep experience with cloud security (GCP preferred), Kubernetes security, and container runtime hardening
Hands-on ability to read, write, and audit code in Python and Rust (or strong systems-level language)
Experience with network security in distributed systems — service mesh, mTLS, network segmentation across heterogeneous hardware
Proven track record managing external penetration tests and translating findings into engineering action
Strong fundamentals in cryptography, identity/access management, and secure software development lifecycle
Experience securing GPU infrastructure or ML training pipelines
Background in offensive security — CTFs, bug bounties, red team engagements
Familiarity with AI-specific threat models (model extraction, training data poisoning, sandbox escape)
Experience building security programs from scratch at a high-growth startup
SOC 2, ISO 27001, or FedRAMP compliance experience
Open-source security tooling contributions
Familiarity with eBPF, Falco, or similar runtime security tools
Cash Compensation Range of $180-350k+ with significant equity incentives
Flexible work arrangement (remote or San Francisco office)
Full visa sponsorship and relocation support
Professional development budget for courses and conferences
Regular team off-sites and conference attendance
Opportunity to shape the future of decentralized AI development
You'll be the foundational security hire at a company where security is existential — not a checkbox. As we scale to support frontier AI labs and enterprises running their most critical training workloads on our platform, you'll build the security org from the ground up and have direct influence on company strategy.
We value intensity and ownership over credentials. If you've built security programs at infrastructure companies, broken into systems professionally, or have deep conviction about how AI infrastructure should be secured — we want to talk.
Ready to help shape the future of AI? Apply now and join us in our mission to make powerful AI models accessible to everyone.
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