Staff Algorithm Developer – Resilience‑Oriented Graph Evolution

corpora-jobs-1778796293285-db9d41c6 - Frontier Development

Algorithm DeveloperStaff1 position

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Why This Role is Different

Frontier Development Role

You will build the first real‑time, submodular‑optimization‑driven graph evolution engine for multi‑agent systems, enabling them to reconfigure their topology on the fly in response to attacks or failures.

The Frontier Element

This role pushes the frontier by applying submodular optimization—traditionally a static, offline technique—to the dynamic, distributed setting of edge‑deployed MAS.

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Project Context

Research Area

Resilience‑Oriented Graph Evolution (ROGE) with Submodular Optimization

From: Communication Graph Vulnerability to Malicious Agents

Why This Role is Critical

ROGE requires a novel combination of submodular optimization, dynamic graph theory, and real‑time decision making—an area that sits at the intersection of theoretical computer science and practical MAS control.

What You Will Build

An autonomous edge‑reconfiguration engine that selects optimal edge additions/removals in real time to maximize a global resilience objective while keeping communication overhead minimal.

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Key Responsibilities

Model the MAS communication graph as a dynamic, submodular optimization problem and derive near‑optimal greedy algorithms.
Implement a low‑latency, distributed reconfiguration engine that runs on resource‑constrained agents.
Integrate the engine with LRC certificates and trust scores to inform edge‑selection decisions.
Validate resilience improvements through large‑scale simulation and real‑world testbeds.
Publish findings and open‑source the core optimization library.

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Required Skills & Experience

Technical Must-Haves

Submodular optimization and greedy approximation algorithms

Expert

Designing efficient, provably near‑optimal edge‑selection strategies.

Dynamic graph algorithms and real‑time decision making

Expert

Ensuring the engine can operate within milliseconds on embedded devices.

Distributed systems and consensus

Advanced

Coordinating reconfiguration actions across a MAS without central control.

C++/Rust performance engineering

Expert

Implementing the core engine to meet strict latency and memory budgets.

Simulation and benchmarking of large‑scale networks

Expert

Evaluating resilience gains under diverse attack scenarios.

Experience Requirements

8+ years in algorithmic research or industrial R&D focusing on submodular optimization or graph algorithms.
Published work on submodular or combinatorial optimization in peer‑reviewed venues.
Hands‑on experience deploying distributed control algorithms on edge or IoT devices.

Education

PhD in Computer Science, Applied Mathematics, or Electrical Engineering with a focus on combinatorial optimization or network control.

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Preferred Skills

Experience with power‑grid or microgrid reconfiguration using submodular methods.
Knowledge of reinforcement learning for graph control.
Familiarity with graph neural networks for dynamic topology inference.

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You Will Thrive Here If...

Excels in high‑autonomy environments where research must translate directly into production code.
Demonstrates a strong bias toward rapid prototyping and iterative improvement.
Comfortable navigating ambiguity and driving end‑to‑end ownership.

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Impact & Growth

12-Month Impact

Within 12 months, deliver a production‑ready graph‑evolution engine that can reconfigure a 10,000‑node MAS in under 500 ms, boosting resilience to Byzantine attacks by at least 30% compared to static topologies.

Growth Opportunity

Scale the engine into a platform for autonomous network control, leading a team that expands the technology to multi‑domain applications such as smart grids, autonomous vehicle fleets, and large‑scale sensor networks.

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.