About the job
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.
Responsibilities
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.
Qualifications
Minimum
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
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.