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Reflection AI

Builds AI models and deployment software

MTS Lead – Technology & Security Engineering

Security Engineering & ArchitectureConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • 20+ years of deep engineering experience at high-valuation companies or in defense-grade environments.
  • A track record of building and operating security engineering functions at scale.
  • Experience supporting SOC 2, ISO 27001, FedRAMP, ISO 22237, ISO 22301, NIST CSF, customer audits, and enterprise security reviews.
  • Experience leading vendor risk, procurement security reviews, and legal contract negotiations.
  • Experience with front-line defenses for an AI company, including physical security and data center security operations.
  • Deep familiarity with Linux and macOS internals, including securing specialized NVIDIA GPU hardware without breaking developer environments.
  • Expert-level knowledge of public cloud architectures, IAM governance at scale, and Kubernetes and container isolation primitives.
  • Technical understanding of cloud and infrastructure security, Kubernetes and container security, zero-trust architectures, security operations at scale, identity and access management, detection and response technologies, and security automation and orchestration.
  • Ability to operate as both an executive leader and a hands-on builder, moving between strategy and implementation.
  • A guardrails-not-gates approach that protects security while preserving researcher workflows, using virtualization, sandboxing, and data isolation.
  • An adversarial approach that assumes endpoints may be compromised, limits blast radius, eliminates persistent access, and detects post-compromise activity.
  • Comfort operating in ambiguous, fast-moving environments where security infrastructure is maturing alongside a rapidly scaling research organization.

Responsibilities

  • Design and manage a resilient corporate device fleet spanning Linux, macOS, Windows, and specialized hardware such as NVIDIA GPU workstations.
  • Shift device fleet controls from restrictive MDM policies toward intelligent posture verification and cryptographic device binding.
  • Secure diverse local toolchains and developer environments without disrupting researcher and engineer workflows.
  • Architect cloud-native security controls across multi-cloud environments containing large GPU clusters.
  • Establish IAM governance, network segmentation, and isolation boundaries appropriate to the scale and sensitivity of training infrastructure and model assets.
  • Partner with infrastructure and research teams to scale security controls with GPU capacity without constraining it.
  • Implement continuous, context-aware authorization using modern mesh networks and proxies such as Tailscale and Cloudflare One.
  • Enforce hardware-backed authentication using WebAuthn and FIDO2 keys across corporate and production environments.
  • Replace standing and persistent access with just-in-time, verifiable authorization.
  • Ensure infrastructure, endpoint configurations, IAM policies, and cloud environments are declared in code using Terraform or Pulumi.
  • Eliminate configuration drift through automated CI/CD validation and continuous compliance checks.
  • Build repeatable, auditable deployment pipelines that make security posture provable.
  • Build telemetry pipelines that ingest high-fidelity logs into a cloud-native data lake to support detection at scale.
  • Detect post-exploitation techniques, lateral movement, and living-off-the-land attacks across corporate and production environments.
  • Tune detection coverage against an assumed-breach threat model, prioritizing time-to-detect and blast-radius containment.
  • Support security compliance frameworks, customer audits, and enterprise security reviews.
  • Lead vendor risk assessments, procurement security reviews, and security-related legal contract negotiations.
  • Own front-line defenses, including physical security and data center security operations.

About the Company

Reflection AI

Builds AI models and deployment software

Reflection AI develops language models for software developers, companies and governments that want control over how AI is deployed and adapted. Its research concentrates on coding, reasoning and AI agents that work through tasks with software tools. The lab builds the surrounding software and infrastructure as well as models, letting customers run AI through its hosted platform or in their own computing environments. Its commercial work pairs model access with engineers who design applications and support customized enterprise deployments from development through operation. Reflection AI was founded in 2024 and is headquartered in New York City.

Company size
201-500
Company stage
Late Stage VC
Total funding
$4.6B
Headquarters
New York City, New York
Founded
2024

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