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Member of Technical Staff — Security Engineering

Causal Labs· Infrastructure
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San Francisco, SingaporeFull-timePosted 2mo ago

About the role

Security-focused MTs design, implement, and operate the security posture across Causal Labs’ engineering stack, protecting research environments, model weights, software infrastructure, and customer integrations while maintaining engineering velocity.

Responsibilities

  • Enterprise Infrastructure & Cloud Isolation: Architect secure network perimeters, private data transmission channels (e.g., PrivateLink, VPNs), and isolated single/multi-tenant storage environments. Implement access controls and customer-managed encryption (KMS/BYOK) across petabyte-scale data stores.
  • Model IP & Weight Protection: Design zero-trust boundaries, encrypted storage, and secure execution environments to protect proprietary foundation model weights and checkpoints against exfiltration during storage, distributed multi-node training, and serving.
  • Pipeline Integrity & AI Threat Defense: Secure our data ingestion pipelines against tampering and data poisoning. Threat-model and defend against AI-specific vulnerabilities, including adversarial inputs, model extraction attacks, and data memorization/regurgitation risks using output guardrails and privacy-preserving techniques.
  • Enterprise Auth & Governance: Own enterprise identity federation (SAML 2.0/OIDC with Okta, Entra ID) and machine-to-machine authentication (mTLS). Implement immutable audit logging, cryptographic erasure, and compliance controls required for enterprise CISOs and SOC 2 / FedRAMP environments.
  • Security Engineering & SDLC: Partner with Infrastructure, Research, and Forward Deployed teams to build automated security testing, threat detection, and vulnerability scanning directly into our deployment workflows, orchestrators (Kubernetes, Slurm), and customer-facing APIs.

Qualifications

  • Demonstrated Hands-on Security Engineering: Proven track record building and securing production systems in cloud environments (AWS, GCP, or Azure) or large-scale distributed systems. Practical mastery of core primitives: IAM, network perimeters, KMS/encryption, and secrets management.
  • Strong Systems & Software Background: Hands-on experience with Linux systems, networking, container security (Docker, Kubernetes), Infrastructure-as-Code (Terraform or Pulumi), and proficiency in languages like Python, Go, or Rust.
  • Understanding of ML Platform Security: Practical grasp of the security challenges unique to ML platforms—protecting high-value model weights, securing distributed training pipelines, data lineage/poisoning, and AI-specific threat vectors.
  • Adaptable & High Agency: Comfort conducting architectural security reviews, digging into complex distributed systems, and adapting fast to new technical constraints or bespoke enterprise customer environments.
  • Bias Toward Real-World Impact: Pragmatic mindset—delivering security solutions that actually work for users under pressure, balancing rigor with engineering velocity.
  • End-to-End Ownership: Ability to take deliverables autonomously from initial threat modeling and requirements all the way through execution, deployment, and monitoring.

Skills

Required:Security engineering and designCloud security (AWS, GCP, or Azure)Linux, networkingContainer security (Docker, Kubernetes)Infrastructure-as-CodeProgramming (Python, Go, or Rust)
Last updated Oct 2, 2026. Interested? Apply on the company site.

About Causal Labs

Towards general causal intelligence

Causal Labs is building a Large Physics foundation Model (LPM) to learn causality through physics and weather

causalityphysicsweatherAIfoundation model
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