Senior Security Engineer

TensorWave, Inc.

Job Overview

Location

Las Vegas, Nevada

Job Type

Full-time

Full Job Description

📋 Description

• Architect and build security controls across the entire stack, partnering with Core Architecture and DevOps to integrate threat modeling, design reviews, and automated guardrails that enable fast engineering without compromising security.
• Own hardware lifecycle security for AMD Instinct GPU clusters and high-speed fabrics (InfiniBand / RoCE), developing code for TPM 2.0, Secure Boot, cryptographic attestation, and programmable trust mechanisms.
• Lead the security posture of open firmware stacks including OpenBMC, coreboot, and ROCm, ensuring they remain secure, auditable, and transparent through custom tooling and automation.
• Engineer Zero-Trust Identity and Access Management (IAM) systems with least-privilege automation, implementing identity-based micro-segmentation integrated directly into orchestration engines.
• Automate compliance with SOC2 and ISO 27001 standards by replacing manual evidence collection with continuous monitoring and always-on controls, acting as the CISO’s technical lead for audit readiness.
• Operate as a purple teamer by simulating attacker behaviors, then developing telemetry, detection rules, and hardening measures to close identified gaps.
• Promote Security-as-Code practices by enforcing version-controlled policies, shift-left integration in CI/CD pipelines, and self-healing infrastructure—eliminating manual configuration as a security risk.
• Collaborate with Engineering, Platform, and business teams to embed security into the design and deployment of AI compute infrastructure at scale.
• Develop and ship production-grade code in Go, Python, or Rust to secure APIs, orchestration planes, and firmware layers, prioritizing autonomous systems over manual dashboards.
• Maintain and enhance Infrastructure-as-Code (Terraform, Pulumi, Ansible) and Kubernetes configurations at production scale to enforce security as a foundational layer.
• Ensure hardware and firmware management is transparent by default, enabling auditability and trust across distributed GPU environments.
• Drive adoption of automated security controls across the organization, reducing reliance on manual processes and increasing system resilience.
• Contribute to the evolution of TensorWave’s hardened security baseline into a mature, enterprise-grade security estate aligned with high-performance computing demands.

🎯 Requirements

• Deep experience securing High-Performance Computing or large GPU environments, including Linux internals and high-speed fabric isolation (InfiniBand / RoCE).
• Strong coding chops in Go, Python, or Rust, with a track record of building autonomous systems rather than manual monitoring tools.
• Hands-on experience with Infrastructure-as-Code (Terraform, Pulumi, Ansible) and Kubernetes at production scale.
• Working knowledge of hardware roots of trust: TPM 2.0, Secure Boot, measured boot, and attestation.
• Familiarity with open firmware stacks (OpenBMC, coreboot) and the AMD / ROCm ecosystem.
• Proven track record building Zero-Trust architectures and automating SOC2 / ISO 27001 compliance programs.
• Background in a fast-scaling startup or high-end security consultancy where you built security systems from scratch and wrote the code to maintain them.
• Architect-builder mindset: equally comfortable in high-level design sessions and pull request reviews.

🏖️ Benefits

• Stock Options
• 100% paid Medical, Dental, and Vision insurance for Employees
• Company Health Savings Account Contributions
• 100% paid Short Term and Long Term Disability Insurance for Employees
• Life and Voluntary Supplemental Insurance Options
• Flexible Spending Account
• 401(k)
• Employee Assistance Program
• Flexible PTO
• Paid Holidays
• Parental Leave
• Other In-Office Perks

Skills & Technologies

Python

Rust

Kubernetes

Terraform

Linux

Senior

Hybrid

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TensorWave, Inc.

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About TensorWave, Inc.

TensorWave develops and operates an AI accelerator cloud built on AMD Instinct GPUs, targeting large-scale model training and inference. The platform offers on-demand and reserved compute with high-bandwidth memory, InfiniBand networking, and container orchestration, delivered through a web console and API. Designed for generative AI, LLM fine-tuning, and HPC workloads, the service emphasizes AMD performance at competitive pricing, supported by 24/7 operations teams. Based in Las Vegas, Nevada, the company serves hyperscalers, research labs, and enterprises needing GPU capacity beyond NVIDIA ecosystems.

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