Member of Technical Staff (Security)

Fireworks AI; United States; On-site; USD 130,000 - 170,000; Full time

Job summary

Fireworks AI is seeking a Security Engineer to design, implement, and operate security controls across AI infrastructure, AI platforms and internal systems.

The role partners with multiple teams to strengthen our security posture as we scale, ensuring confidentiality, integrity, and availability of data, models, and infrastructure in a multi-cloud environment. You will embed security across the stack and contribute to continuous security improvements.

Qualifications

• Hands-on experience with Kubernetes, Docker, and containerized production environments.
• Deep knowledge of Kubernetes internals and native security controls is a strong plus.
• Familiarity with security tooling in managed CI/CD environments (GitHub Actions, Harness, CircleCI).
• Proficient in Python and/or Go with experience in designing production-grade systems.
• Familiarity with IAM concepts: SAML, OAuth, OIDC, SSO, RBAC/ABAC.

Responsibilities

• Design and implement security controls across AI infrastructure, platforms, and internal systems.
• Embed security into CI/CD pipelines with automated scanning and policy enforcement.
• Lead vulnerability management across applications, containers, and cloud infrastructure.
• Participate in red/blue team exercises and post-incident analyses to improve resilience.
• Develop in-house security tooling to avoid over-reliance on off-the-shelf solutions.
• Build security automation platforms for incident response and security operations.

Skills

Kubernetes

Docker

Security tooling

Cloud-native architectures

Python/Go

IAM / SSO / RBAC

Terraform

CI/CD security

Incident response

Red/Blue team

Tools

GitHub Actions

Harness

CircleCI

SBOM tooling

Job description

• As the Security Engineer at Fireworks AI, you will play a key role in designing, implementing and operating security controls across AI infrastructure, AI platforms and internal systems
• You will work closely with the multiple teams to strengthen our security posture and support our rapid growth. As more organizations rely on large language models and cloud-native AI services, ensuring the confidentiality, integrity, and availability of data, models, and infrastructure is paramount
• This role plays a critical part in building that trust by designing and embedding security across layers of our technology stack
• Design and build security-focused software and platform capabilities to protect customer data, models, and services across our multi-cloud infrastructure, including encryption, identity and access management, secure API gateways, secure model execution, and sandboxing strategies
• Perform security reviews of cloud-native architectures—including Kubernetes clusters, multi-cloud workloads, and distributed data stores—and build integrated systems for continuous security monitoring, anomaly detection, and automated response
• Embed security into CI/CD pipelines using a DevSecOps approach, implementing automated scanning, policy enforcement, and secure-by-default build and deployment workflows
• Apply a build-over-buy philosophy by designing and developing in-house security tooling and automation where it provides better control, scalability, and integration than off-the-shelf solutions
• Build and operate a comprehensive vulnerability management program, partnering with various teams to remediate risks across applications, containers, cloud infrastructure, and dependencies
• Operate and continuously improve security operations, including detection engineering, alert triage, incident response, and continuous improvement through post-incident reviews
• Participate in red/blue team exercises, tabletop simulations, and post-incident root cause analysis to strengthen security resilience
• Embed compliance and regulatory controls into infrastructure and product layers (e.g., SOC 2, ISO 27001, ISO42001, HIPAA, PCI-DSS, GDPR)
• Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving
• Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally
• Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results
• Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation

Hands-on experience with Kubernetes, Docker, and containerized production environments; deep knowledge of Kubernetes internals and native security controls is a strong plusFamiliarity with security tooling in managed CI/CD environments (e.g., GitHub Actions, Harness, CircleCI)Strong understanding of cloud-native architectures using GCP, particularly in the area of network segregation, authentication, authorization, encryption, data protection, intrusion detection, and cloud-specific security benchmarksSolid experience working in Linux environments, including system administration, debugging, and automation via command-line toolingProficient in Python and/or Go with experience in designing production-grade systemsFamiliarity with modern identity and access controls (SAML, OAuth, OIDC, SSO, RBAC/ABAC)3 to 7 years of experience in software engineering or security engineering with a strong focus on security, infrastructure, or cloud-native systemsExperience designing secure multi-cloud deployments and zero-trust architecturesExperience designing, operating, and securing large-scale Kubernetes platforms, including control plane security, node hardening, and multi-tenant isolationProficiency with infrastructure-as-code using Terraform and Python, including experience building modular policy-as-code frameworksExperience designing, operating, and securing large-scale multi-cloud platforms across AWS, GCP, Azure, Oracle Cloud, and GPU as service cloud providersStrong understanding of data protection techniques, including encryption at rest/in transit, tokenization, key management, and confidential computingExperience integrating security into microservice architectures, service meshes, and distributed systemsHands-on experience securing LLM/ML platforms, model inference infrastructure, GPU clusters, or data labeling pipelinesExperience designing detection engineering pipelines across cloud audit logs, network telemetry, and application signalsExperience building large-scale IAM and PAM platforms using least-privilege, workload identity, and just-in-time accessFamiliarity with container image vulnerability remediation, security, SBOM generation, and software supply chain securityExperience building, implementing and operating security automation platforms for incident response and security operationsFamiliarity with compliance tooling and frameworks (e.g., Vanta, SOC 2, ISO 27001, ISO 42001, PCI-DSS)