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Principal Research Engineer – Evolutionary Adversarial Training & Dynamic Role Assignment (DRAT)

corpora-jobs-1778796293285-db9d41c6 - Frontier Development
Research ScientistPrincipal1 position
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Why This Role is Different

Frontier Development Role

Lead the design and deployment of the most advanced adversarial training loop for multi‑agent systems. You’ll build the evolutionary attacker generator, craft dynamic role policies, and ensure the resulting agents converge under Byzantine threat models while remaining interpretable.

The Frontier Element

This role pioneers the first end‑to‑end, evolutionary‑driven multi‑agent training framework that adapts roles in real time, a capability that has never been demonstrated at scale in hostile environments.

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Project Context

Research Area

Dynamic Role-Based Adversarial Training (DRAT)

From: Adaptive Multi‑Agent Defense Against Adversarial Coordination

Why This Role is Critical

DRAT is the core of RACE’s adaptive learning loop, requiring a deep blend of multi‑agent reinforcement learning, evolutionary adversary design, and on‑the‑fly role re‑allocation to prevent over‑specialization and expose agents to unseen attack patterns.

What You Will Build

A production‑grade DRAT pipeline that (1) generates evolutionary attacker populations, (2) orchestrates dynamic role assignment across Orchestrator, Executor, Ground, Critic, and Memory agents, and (3) integrates the hardened policies into the RACE coordination engine.

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Key Responsibilities

  • Design and implement the evolutionary attacker generator using GANs and policy‑search techniques to produce diverse, worst‑case adversarial scenarios.
  • Develop a dynamic role‑assignment scheduler that reallocates agent functions (Orchestrator, Executor, etc.) based on task demands and threat context.
  • Integrate DRAT with the RACE engine, ensuring seamless policy updates and runtime monitoring of convergence metrics.
  • Validate Byzantine‑resilient convergence through rigorous simulation and formal analysis, publishing proofs and benchmarks.
  • Collaborate with the Systems Engineer to embed DRAT outputs into the federated learning and sensor‑fusion layers.
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Required Skills & Experience

Technical Must-Haves

Multi‑agent reinforcement learning (MARL)

Expert
Designing and training policies that converge under Byzantine conditions.

Evolutionary algorithms & adversarial policy search

Advanced
Generating adaptive attacker populations that evolve over training epochs.

Generative Adversarial Networks (GANs)

Proficient
Synthesizing realistic adversarial data streams for hardening.

Formal verification & probabilistic convergence proofs

Expert
Providing provable guarantees for DRAT‑trained agents.

Python, PyTorch/TensorFlow, Ray/RLLib

Expert
Implementing large‑scale distributed training pipelines.

Experience Requirements

  • 10+ years in AI/ML research with a focus on multi‑agent systems or adversarial learning.
  • Track record of publishing peer‑reviewed work on Byzantine‑resilient MARL or evolutionary adversarial training.
  • Experience leading end‑to‑end research projects from theory to production.

Education

PhD in Computer Science, Robotics, or related field with a strong emphasis on reinforcement learning or adversarial machine learning.

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Preferred Skills

  • Experience with UAV swarm simulation environments (e.g., AirSim, Gazebo).
  • Knowledge of MPAC multi‑principal coordination protocols.
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You Will Thrive Here If...

  • Thrives in high‑autonomy, low‑hand‑off environments.
  • Demonstrates a bias toward rapid iteration and shipping working systems.
  • Comfortable pushing the boundaries where textbooks end.
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Impact & Growth

12-Month Impact

Within 12 months, deliver a fully automated DRAT pipeline that reduces adversarial success rate by >70% in simulated UAV swarm scenarios, with provable convergence guarantees under bounded Byzantine fractions.

Growth Opportunity

Lead the expansion of DRAT to new domains (cyber‑physical networks, decentralized finance), mentor a growing research team, and shape the next generation of adaptive multi‑agent training frameworks.

Ready to Push the Boundaries?

If this sounds like the challenge you have been looking for, we want to hear from you. We value what you can build over where you have been.