Software Security Researcher, GPU and AI Systems · Full-time

Stealthium, Inc.; United States; Remoto; USD 150.000 - 210.000; Tempo pieno

Descrizione del lavoro

Stealthium, Inc. is seeking a security researcher to own GPU and AI infrastructure security research. You will transform findings into product-level detections, work on driver/kernel interfaces, and drive threat models that improve runtime telemetry.

You will develop proofs-of-concept, analyze attack surfaces, and collaborate with engineering to ship concrete security capabilities across multi-cloud environments.

Competenze

• 3+ years in security research, vulnerability research, exploit development, or offensive security engineering.
• Degree in computer science, electrical engineering, or related field, or equivalent hands-on experience.
• Strong systems fundamentals: Linux internals, memory management, kernel/userspace boundaries.
• Practical experience with driver/kernel research, firmware security, virtualization, or hardware-adjacent attack surface.
• Comfort reading/writing systems code in C/C++, Rust, Go, or Python, and building tooling.
• Working knowledge of applied cryptography, attack vectors, and mitigation techniques.
• Ability to work independently in an early-stage environment.

Mansioni

• Conduct applied security research across GPU platforms, accelerator runtimes, and AI infrastructure components.
• Build proof-of-concept exploits and attack simulations, then translate to detection logic.
• Perform threat modeling for AI compute environments: multi-tenant GPU isolation, model and container supply chains.
• Reverse engineer components of the GPU/AI software stack to identify weaknesses.
• Partner with engineering to define observed telemetry and its importance.
• Contribute to core product capabilities around runtime insight, anomaly detection, and supply chain integrity.
• Track external threat landscape for AI infrastructure and inform the roadmap.
• Represent Stealthium's research externally through publications and disclosures.

Conoscenze

Security research

Linux internals

Kernel/userspace boundaries

Attack surface exploration

C/C++/Rust/Go/Python

Threat modeling

Independent work

Formazione

CS/EE degree or equivalent

Strumenti

C/C++/Rust/Python tooling

Descrizione del lavoro

Stealthium is building the security and observability control plane for AI compute.

Modern AI infrastructure runs on accelerators that traditional security tooling was never designed to see. GPU workloads, model supply chains, CUDA kernels, drivers, and multi-tenant scheduling layers all sit in a blind spot. We are closing it, with a multi-cloud, multi-accelerator platform that turns runtime telemetry from AI compute into real security insight.

WHAT YOU WILL BE WORKING ON

You will own security research into the GPU and AI infrastructure stack, and turn what you find into product.

This means understanding how NVIDIA GPUs and adjacent accelerator platforms behave under attack: the driver and runtime layers, container and virtualization boundaries, GPU sharing and multi-tenancy models, model and dependency supply chains, and the workload patterns of real training and inference systems.

Your research directly shapes what the Stealthium platform detects. You will not be writing findings that sit in a document. You will be defining the threat models, building the proof of concepts, and working alongside engineering to convert them into runtime detections that ship.

This is a hands-on research role. Expect to spend your time in drivers, kernels, container runtimes, firmware interfaces, and real GPU clusters.

WHAT YOU WILL DO

• Conduct applied security research across GPU platforms, accelerator runtimes, and AI infrastructure components
• Build proof-of-concept exploits and attack simulations that demonstrate real risk in GPU and AI workloads, then translate them into detection logic
• Perform threat modeling for AI compute environments: multi-tenant GPU isolation, model and container supply chains, driver and firmware attack surface, orchestration and scheduling layers
• Reverse engineer components of the GPU and AI software stack to understand undocumented behavior and identify weaknesses
• Partner with engineering to define what the platform should observe, why it matters, and how an attack looks in telemetry
• Contribute to core product capabilities around runtime insight, anomaly detection, and supply chain integrity
• Track the external threat landscape for AI infrastructure and bring that intelligence back into the roadmap
• Represent Stealthium's research externally through publications, disclosures, and conference work

WHAT WE ARE LOOKING FOR

• 3+ years in security research, vulnerability research, exploit development, or offensive security engineering
• Degree in computer science, electrical engineering, or a related field, or equivalent hands-on experience
• Strong systems fundamentals: Linux internals, memory management, kernel and userspace boundaries, process and container isolation
• Practical experience with at least one of: driver or kernel research, firmware security, virtualization and container escape research, or hardware-adjacent attack surface
• Comfort reading and writing systems code, in C, C++, Rust, Go, or Python, and building your own tooling
• Working knowledge of applied cryptography, common attack vectors, and mitigation techniques
• Ability to work independently in an early-stage environment, take a vague problem, and return with something concrete

NICE TO HAVE

• Direct experience with NVIDIA GPU internals: CUDA, drivers, MIG, vGPU, GPUDirect, DOCA, or BlueField
• Familiarity with AI and ML infrastructure: training and inference pipelines, model formats and serialization risks, ML dependency supply chains
• Experience with Kubernetes, container runtimes, and GPU scheduling in production
• Cloud security experience, especially in multi-tenant or shared infrastructure environments
• Background in observability or detection engineering: metrics, logs, traces, profiling, eBPF
• Published research, CVEs, conference talks, or open source contributions
• M.Sc. or Ph.D. in computer science, electrical engineering, or a related technical field
• Experience with high-performance computing environments or large-scale GPU deployments
• Work on genuinely novel problems at the intersection of AI infrastructure, observability, and security, in a space where the attack surface is still being mapped
• Shape a product from the ground up, with research that turns into shipped capability rather than a report
• Join a small, highly technical team with backgrounds from Amazon, Intel, AMD, Microsoft, and Elastic
• Fully remote, with real ownership and direct influence on technical direction
• Competitive compensation and meaningful equity