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Microloft

Senior Software Engineer

Posted 10 Days Ago
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Remote
Hiring Remotely in United States
120K-261K Annually
Senior level
Remote
Hiring Remotely in United States
120K-261K Annually
Senior level
Lead performance analysis and optimization for large-scale HPC and AI workloads across InfiniBand-connected GPU clusters. Diagnose network bottlenecks, tune RDMA and QoS, analyze fabric telemetry, optimize collective communication, develop performance tooling, and benchmark distributed workloads. The role also collaborates with operations and hardware vendors on interconnects, firmware, topology, and scalable communication performance.
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Overview

We are looking for a Senior Performance Engineer with deep expertise in High-Performance Computing (HPC) and Artificial Intelligence (AI) networking performance, particularly across InfiniBand-based GPU clusters. You will be a key technical leader focused on understanding, analyzing, and optimizing the performance of distributed workloads running at massive scale - often involving tens of thousands of GPUs interconnected via high-speed networks.

This role requires strong familiarity with Message Passing Interface (MPI), NVIDIA Collective Communications Library (NCCL), collective communication algorithms, and the underlying transport technologies (Remote Direct Memory Access (RDMA) over InfiniBand). You should have extensive experience with network-level debugging, topology-aware optimization, and low-latency, high-throughput communication tuning in Linux environments. If you enjoy solving hard problems at the convergence of network systems and distributed applications, we want to talk to you.


Responsibilities
  • Diagnose and resolve communication-related performance bottlenecks across large-scale distributed systems.
  • Analyze InfiniBand fabric behavior, including congestion, credit starvation, route imbalance, or link-level errors.
  • Tune and debug Remote Direct Memory Access (RDMA) transport layers, message ordering, path migration, and Quality of Service (QoS) policies on multi-rail or multi-subnet fabrics.
  • Develop custom tooling or scripts to analyze fabric telemetry, traffic flows, and job-induced network behavior.
  • Collaborate with operations team to ensure optimal use of fabric features (adaptive routing, congestion control, service level tuning). 
  • Work closely with hardware vendors (e.g., NVIDIA) to evaluate and validate next-gen interconnects and switch firmware.
  • Support benchmarking of High-Performance Computing (HPC) / Artificial Intelligence (AI) workloads, including characterization of latency, bandwidth, and collective efficiency under scale.


Qualifications

Required Qualifications:

  • Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
    • OR equivalent experience.

Other Requirements:

  • Ability to meet Microsoft, customer and/or government security screening requirements are required for this role.
  • These requirements include, but are not limited to the following specialized security screenings: 
    • Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.

Preferred Qualifications:

  • Master's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
    • OR Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
    • OR equivalent experience.
  • Proven history of debugging network performance issues at scale (e.g., link-level failures, fabric hot spots, routing asymmetry).

  • Expert-level experience with Message Passing Interface (MPI), NVIDIA Collective Communications Library (NCCL), and collective algorithm behavior in multi-node, multi-GPU systems
  • Deep knowledge of InfiniBand architecture, including verbs, Remote Direct Memory Access (RDMA), Scalable Hierarchical Aggregation Protocol (SHARP), adaptive routing, congestion control, and Quality of Service (QoS).
  • Familiarity with job schedulers and network-aware scheduling/topology constraints.
  • Familiarity with GPU communication stack internals: GPU Direct RDMA and NVLink topologies.
  • Experience with real-world performance tuning of transformer training, large-scale AllReduce, or model/data parallel Artificial Intelligence (AI) workloads.

#azurecorejobs


Software Engineering IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay


This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.



Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

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