ZipRecruiter

Log In

1

Distributed Systems Engineer Jobs in Milpitas, CA

IonQ

Senior Distributed Systems Engineer

Santa Clara, CA

$122K - $167K/yr

We are looking for a deeply hands-on Senior Distributed Systems Engineer to join the team building IonQ's Network and Security Platform. You will own the backend services - ingestion pipelines ...

IonQ

Senior Distributed Systems Engineer

Santa Clara, CA · On-site

$122K - $167K/yr

We are looking for a deeply hands-on Senior Distributed Systems Engineer to join the team building IonQ's Network and Security Platform. You will own the backend services - ingestion pipelines ...

IonQ

Senior Staff Distributed Systems Engineer

Santa Clara, CA · On-site

$122K - $167K/yr

We are looking for a deeply hands-on Senior Staff Distributed Systems Engineer to join the team building IonQ's Network and Security Platform. You will own the backend services - ingestion pipelines ...

next page

Showing results 1-20

All JobsDistributed Systems Engineer JobsDistributed Systems Engineer Jobs in Milpitas, CA

Distributed Systems Engineer information

See Milpitas, CA salary details

$62.3K

$148.3K

$194.6K

How much do distributed systems engineer jobs pay per year?

As of Aug 5, 2026, the average yearly pay for distributed systems engineer in Milpitas, CA is $148,253.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,200.00 and $183,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a distributed systems engineer?

To thrive as a Distributed Systems Engineer, you need a strong background in computer science, experience with large-scale system design, and proficiency in languages such as Java, Go, or Python. Familiarity with cloud platforms (like AWS, GCP, or Azure), container orchestration tools (such as Kubernetes), and distributed databases is commonly required, and certifications in cloud computing can be advantageous. Strong problem-solving abilities, collaboration, and excellent communication skills help you navigate complex issues and work effectively across technical teams. These skills are fundamental for designing, implementing, and maintaining robust distributed systems that perform reliably at scale.

What does a distributed systems engineer do?

A Distributed Systems Engineer designs, builds, and maintains large-scale systems that run across multiple machines or data centers. They ensure reliability, scalability, and fault tolerance by using technologies like cloud computing, containerization, and distributed databases. Their work often involves solving complex problems related to data consistency, network latency, and system coordination.

What are popular job titles related to Distributed Systems Engineer jobs in Milpitas, CA? For Distributed Systems Engineer jobs in Milpitas, CA, the most frequently searched job titles are:
What job categories do people searching Distributed Systems Engineer jobs in Milpitas, CA look for? The top searched job categories for Distributed Systems Engineer jobs in Milpitas, CA are:
What cities near Milpitas, CA are hiring for Distributed Systems Engineer jobs? Cities near Milpitas, CA with the most Distributed Systems Engineer job openings:
Distributed Systems Engineer jobs near you
Infographic showing various Distributed Systems Engineer job openings in Milpitas, CA as of July 2026, with employment types broken down into 100% Full Time. Highlights an 68% In-person, and 32% Remote job distribution, with an average salary of $148,253 per year, or $71.3 per hour.

Senior Distributed Systems Engineer

MBZUAI (Mohamed bin Zayed University of Artificial Intelligence)

Sunnyvale, CA • On-site

$122K - $166K/yr

Full-time

Re-posted 14 days ago

Job description

Job Summary:
MBZUAI (Mohamed bin Zayed University of Artificial Intelligence) is focused on designing and operating ultra-scale GPU supercomputing systems for training foundation models. The Senior Distributed Systems Engineer will optimize communication stacks for large-scale distributed training, ensuring performance and reliability across GPU workloads.
Responsibilities:
• Design and optimize expert-parallel and hybrid-parallel communication patterns
• Drive high-performance hierarchical collectives for MoE workloads
• Co-design runtime orchestration with communication topology awareness
• Reduce tail latency and improve determinism across thousands of GPUs
• Architect fault-tolerant distributed execution under real-world cluster failures
• Communication-compute overlap and topology-aware collective optimization
• Deep debugging of NCCL, RDMA, and custom communication layers
• Hybrid expert parallel strategies in modern large-scale MoE systems
• Elastic and resilient distributed job orchestration concepts
• Congestion analysis and routing optimization across InfiniBand/RoCE fabrics
• Microbenchmarking and performance modeling for communication-heavy workloads
• Hybrid expert parallel communication for Mixture-of-Experts training
• Scaling behavior under network pressure
• Distributed orchestration for elastic, large-scale training
• Fault detection and recovery in distributed GPU workloads
• Cross-layer bottlenecks: GPU ↔ NIC ↔ PCIe ↔ NVSwitch ↔ Fabric ↔ Scheduler
Qualifications:
Required:
• Experience optimizing distributed training at 1,000+ GPU scale (or equivalent depth)
• Hands-on expertise with RDMA, InfiniBand, RoCE, and GPUDirect RDMA
• Deep familiarity with NCCL and/or UCX internals
• Strong systems programming ability (C/C++, Rust, or Go)
• Strong familiarity with modern model training frameworks such as PyTorch
• Ability to troubleshoot and profile training performance issues related to communication bottlenecks
• Ability to translate research ideas into production-grade optimizations
• Experience debugging distributed hangs, desynchronization, and performance regressions
• Include a link to your GitHub (required)
• Provide links to relevant distributed systems, HPC, or large-scale training projects
• Include a list of publications and/or public technical reports (if applicable)
• Describe the hardest distributed debugging problem you solved
• Include measurable performance improvements you have delivered
• Master’s, or Bachelor’s + 1 year of relevant experience.
Company:
Official account of Mohamed bin Zayed University of Artificial Intelligence. Dedicated to research, innovation, and empowering brilliant minds in AI. Founded in 2019, the company is headquartered in Abu Dhabi, ARE, with a team of 51-200 employees. The company is currently Growth Stage.

Apply