Principal Engineer - AI Networking

• Design, develop, and optimize RDMA-based software components and services for large-scale AI infrastructure.
• Build and enhance collective communication frameworks, transport layers, and communication libraries used by distributed AI workloads.
• Develop congestion management, load balancing, resiliency, and failover capabilities for RDMA-based networks.
• Analyze and improve communication performance across networking, GPU, and software stacks.
• Design and implement scalable distributed systems supporting AI training and inference environments.
• Collaborate with networking, AI infrastructure, hardware, and cloud platform teams to deliver high-performance solutions.
• Investigate and resolve complex networking, performance, and reliability issues in production environments.
• Develop observability, telemetry, debugging, and performance analysis tools for distributed communication systems.
• Contribute to architectural design discussions and technical direction for networking platforms.
• Participate in code reviews and help maintain engineering excellence across the team.

The ideal candidate is an experienced RDMA software engineer with a strong background in high-performance networking, distributed communication systems, and systems programming. You will work closely with senior technical leaders to design, implement, optimize, and operate critical networking infrastructure used by large-scale AI training and inference workloads.

This is a hands-on engineering role requiring deep technical expertise, strong software development skills, and a passion for solving complex performance and scalability challenges.

What You'll Bring

• Strong software engineering fundamentals and systems programming expertise.
• Deep interest in RDMA, high-performance networking, and distributed communication systems.
• Ability to diagnose and solve complex performance and scalability problems.
• Strong collaboration and communication skills in cross-functional engineering environments.
• Ownership mindset with the ability to independently drive technical initiatives from design through production deployment.
• Passion for building infrastructure that enables next-generation AI systems., * Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or related field; advanced degree preferred.
• 7+ years of software engineering experience in systems software, networking, distributed systems, or infrastructure platforms.
• Strong hands-on expertise with RDMA technologies, including RoCEv2 and/or InfiniBand.
• Experience developing RDMA-enabled software, communication libraries, networking services, or distributed infrastructure.
• Strong understanding of RDMA programming concepts, including queue pairs, completion queues, memory registration, verbs, and transport semantics.
• Proficiency in C/C++ and Linux systems programming.
• Experience debugging and optimizing performance-critical software systems.
• Solid understanding of networking fundamentals, operating systems, and distributed systems concepts.

Preferred Qualifications

• Experience with collective communication frameworks and libraries such as NCCL, RCCL, MPI, UCX, UCC, XCCL, or similar technologies.
• Experience supporting AI/ML infrastructure and distributed training environments.
• Knowledge of GPUDirect RDMA and GPU-aware communication technologies.
• Experience developing congestion management, traffic engineering, or network resiliency solutions.
• Familiarity with large-scale GPU clusters and high-performance computing environments.
• Experience building services and infrastructure operating directly over RDMA transports.
• Familiarity with distributed training frameworks such as PyTorch, DeepSpeed, Megatron-LM, TensorFlow, or JAX.
• Experience with Kubernetes, containers, and cloud infrastructure platforms.
• Understanding of performance profiling and benchmarking tools for networking and distributed systems.

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