We are seeking a highly experienced Machine Learning Engineer for a hybrid role focused on advanced temporospatial networking and resource management. This role involves applying cutting-edge ML research to solve critical network orchestration challenges. If you excel at translating novel algorithms into robust, high-impact systems, this is your opportunity.
Key Responsibilities
ML Research: Research and apply state-of-the-art ML and optimization algorithms for network orchestration.
MLOps & Infra: Develop and maintain ML training infrastructure using Kubernetes and advanced MLOps tooling.
System Integration: Integrate AI technology with core platform components, ensuring end-to-end functionality.
Technical Authority: Act as the technical expert on ML technologies, interacting directly with customers and partners.
Documentation: Develop and maintain documentation for novel algorithms.
Required Qualifications
Education: Master's or Ph.D. in CS, Math, Statistics, or a related quantitative field.
ML/Optimization Fluency: Expertise in Python and proficiency with PyTorch, TensorFlow OR optimization libraries (Gurobi, Google OR tools).
Code Quality: Ability to write clean, maintainable, and efficient production code.
Communication: Strong technical communication skills across all functions.
Impact Drive: Desire to pitch and advocate for the technology.
Preferred Experience
Experience in wireless/satellite communications or Software-Defined Networking (SDN).
Prior experience in a technical sales or customer-facing role.
Experience writing tests for software and ML algorithms.
Proficiency with C, C++, or Go.
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