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Elloe AI

Machine Learning Security Engineer, Red Teaming and Patch Risk Forecasting (Remote)

Remote
Machine Learning SHAP
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Description

Member of Technical Staff – ML Security Engineer (Red Teaming + Patch Risk Forecasting)

Location: Austin, Texas

Department: Engineering

Location Type: REMOTE

Employment Type: FULL_TIME

Full-time | Remote | Adversarial ML | Reports to Head of Risk

About Elloe
Elloe is the trust layer for AI.
We sit between the world’s most powerful language models and the institutions that can't afford to get it wrong — hospitals, banks, regulators. We trace and block failures in real time. That’s not marketing — we’re deployed at the European Commission, with NIH clinical trials, and inside a Top-5 EU bank catching GDPR violations live.

This is the enforcement layer GenAI has been missing. We're not visualizing problems — we're fixing them.

About the Role
Elloe’s safety loop only works if we can simulate attacks before they reach real users. You’ll drive red teaming at the system level — from jailbreaking to fuzzing and help shape the defense logic in AutoHeal and ReplayHeatmap.

What You’ll Build
1. Red Team Simulation Engine
  • Launch adversarial attacks (prompt injections, bypass chains, logic traps)
  • Generate incident traces used to train AutoHeal patch logic
  • Contribute to fuzzing harnesses, risk scoring, and breach labeling
2. Security Patch Forecasting
  • Forecast deployment risks based on incident graph trends
  • Automate “patch windows” based on SHAP mismatch clusters
  • Help convert audit traces into product-stoppable violations
3. System-Level Defense
  • Build explainability-linked guardrails across the stack
  • Collaborate with infra and explainability leads on enforcement crossover

Who You Are
  • Deep experience in adversarial ML, red teaming, or fuzz testing
  • Understands how explainability can be a security surface
  • Bonus: experience with threat modeling, diff-testing, or jailbreak detection

Why It Matters
Red teaming isn’t an afterthought. It’s how Elloe gets trusted to run in places that can’t afford to fail.

Why Now
Major regulators are asking for test results before they approve AI deployment. Institutions want defensible logs, not theoretical attacks. This is the moment when red teaming goes from research to required.

You’ll Leave This Role With
  • A safety portfolio tied to real red team incidents, not just demos
  • Impact on critical infrastructure used by hospitals, central banks, and governments
  • A seat at the table defining what trustworthy GenAI actually means

Logistics & Application
  • Start Date: Flexible (Q3 ideal)
  • Location: Remote-first; timezone overlap with NY or DC preferred
  • Comp: Competitive salary + equity
  • To Apply: Share one failure mode you’d simulate in a next-gen red team harness.
Elloe AI
Elloe AI

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