Top 16 Broadcast engineer Jobs in San Francisco Bay Area, Ca

Cluster Engineer
STN Inc —San Francisco Bay Area, CA
- Full-time
- Remote
- Azure
- AWS
- Linux
Quick Apply
13d
Principal Software Engineer, WebRTC
roblox —San Mateo, CA
- $295,250 - $345,040 a year
- Full-time
- Office
- 8 years
- US work authorization
IT Support Engineer, Executive Support
Anthropic —San Francisco, CA
- $230,000 - $265,000 a year
- On call
- Hybrid work
- 7 years
- Mid-level
- Office
- Parental leave
- Flexible schedule
Software Dev Engineer II, Amazon Interactive Video Service (IVS)
Amazon Data Services, Inc. —San Francisco, CA
- $143,700 - $223,600 a year
- Full-time
- Computer science
- Computer Science
- Mid-level
- AD&D insurance
- Health insurance
Lead RF Engineer
Xona Space Systems —Burlingame, CA
- $170,000 - $200,000 a year
- Full-time
- 3 years
- Travel
- Senior level
AV Technician (5163C) Intercollegiate Athletics
University of California Berkeley —Berkeley, CA
- $82,800 - $110,000 a year
- Part-time
- Mid-level
- University
- Bachelor's degree
Staff QA Engineer - Cinema Imaging
Dolby —San Francisco, CA
- $152,900 - $210,200 a year
- Full-time
- 7 years
- Computer Science
- Laboratory experience
8d
Staff Systems Engineer, Microsoft Teams
Visa Technology and Operations LLC —Foster City, CA
- $131,600 - $233,700 a year
- Full-time
- Hybrid work
- Office
- 8 years
- Analysis skills
- Wellness program
- Health savings account
Quick Apply
Music Coordinator (Part-Time)
Golden State —San Francisco, CA
- $25 an hour
- Part-time
- Bachelor's degree
- Fair chance
- Experience equivalent to degree accepted
Quick Apply
Technical Engineer
Audacy, Inc. —San Francisco, CA
- $95,000 - $105,000 a year
- Online Application
- Writing skills
- Mid-level
- 3 years
- Health savings account
- Disability insurance
Sr. RF Engineer
Xona Space Systems —Burlingame, CA
- $170,000 - $200,000 a year
- Full-time
- System design
- Mid-level
- 3 years
Mechanical Engineer II
Xona Space Systems —Burlingame, CA
- $132,000 - $150,000 a year
- Full-time
- Bachelor of Science
- Writing skills
- Mid-level
QA Manager, Cinema Technology
Dolby —San Francisco, CA
- $152,900 - $210,200 a year
- Full-time
- Computer Science
- Laboratory experience
- English
8d
Director, IT
Harvey —San Francisco, CA
- $224,000 - $336,000 a year
- Full-time
- Hybrid work
- Google Workspace
- System design
- Office
- Health insurance
- Dental insurance
Electronics Engineering Technologist
Lawrence Berkeley National Laboratory —San Francisco Bay Area, CA
- $46.47 - $59.77 an hour
- Full-time
- Background check
- Google Workspace
- Bachelor of Science
- Health insurance
- Tuition reimbursement
Senior Technical Program Manager, Creative Technology, Marketing
Google —San Francisco, CA
- $192,000 - $278,000 a year
- Full-time
- Office
- 8 years
- Bachelor's degree
9d

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broadcast engineer jobs near san francisco bay area, ca

Cluster Engineer

STN Inc
San Francisco Bay Area, CA

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Job Details

Full-time
13 days ago

Qualifications

GPU programming
QoS
Network design
Network troubleshooting
GPU architecture
Distributed file systems
PyTorch
High-performance computing clusters
System performance optimization
AI platforms (beyond public GPTs)
Scalable systems
Computational framework
Performance testing
Bash
Infrastructure architecture design
Data caching
Design engineering
Scalability
Network performance optimization
Storage management (system administration)
Linux
Technical troubleshooting support
Systems analysis
Distributed computing
Benchmarking
System tuning
Data performance optimization
Scalability and Performance Testing (system development)
Machine learning frameworks
Linux administration

Full Job Description

Position Summary
We are seeking a highly experienced AI Infrastructure Engineer to architect, deploy, optimize, and operate large-scale GPU clusters supporting state-of-the-art AI training and inference workloads. This is a deeply technical role focused on maximizing cluster efficiency, scalability, and performance across the entire AI stack—from GPU hardware and high-speed networking to distributed training frameworks and inference optimization.
The ideal candidate has built GPU clusters from the ground up, tuned distributed training environments, optimized large-scale inference deployments, and understands how every layer of the infrastructure contributes to application performance.

Responsibilities

Design, deploy, and optimize multi-node GPU clusters for AI training and inference workloads.
Tune distributed training environments to maximize GPU utilization, throughput, and scaling efficiency.
Optimize inference clusters for maximum token generation throughput, low latency, and high GPU utilization.
Build and support production AI infrastructure running hundreds to thousands of GPUs.
Analyze and eliminate performance bottlenecks across compute, networking, storage, and software layers.
Perform NCCL benchmarking, analysis, and tuning to achieve optimal collective communication performance.
Design and optimize GPU networking using InfiniBand or RoCE v2, including RDMA, congestion management, topology awareness, and QoS.
Configure and tune distributed AI software stacks including:
- PyTorch
- NCCL
- CUDA
- UCX
- MPI
- Slurm
- Pyxis/Enroot
Optimize GPU scheduling and resource allocation for both training and inference environments.
Develop repeatable benchmarking and validation processes for new hardware, firmware, drivers, and software releases.
Identify performance regressions and troubleshoot distributed training issues at scale.
Optimize storage architectures for AI workloads, including checkpointing, dataset streaming, and high-performance parallel I/O.
Work closely with ML engineers to improve training scalability and inference efficiency.
Create automation to deploy, validate, benchmark, and monitor GPU clusters.
Evaluate emerging AI infrastructure technologies and recommend improvements to platform architecture.

Required Qualifications
7+ years designing or operating large-scale Linux infrastructure.
5+ years supporting production GPU clusters for AI or HPC workloads.
Demonstrated experience building multi-node GPU training environments from the ground up.
Deep expertise with distributed PyTorch training.
Extensive experience troubleshooting and optimizing NCCL communications.
Strong understanding of distributed AI communication patterns, including:
- AllReduce
- ReduceScatter
- AllGather
- Broadcast
- Point-to-point communications
Experience benchmarking distributed training using tools such as:
- nccl-tests
- NVIDIA DCGM
- Nsight Systems
- MLPerf (preferred)
Strong understanding of GPU memory management, including:
- KV Cache
- Activation checkpointing
- Tensor Parallelism
- Pipeline Parallelism
- Data Parallelism
Experience optimizing LLM inference throughput, including:
- Tokens/sec optimization
- Batch sizing
- Continuous batching
- KV cache tuning
- Memory bandwidth optimization
Experience tuning CUDA, NCCL, UCX, and MPI for maximum distributed performance.
Expert-level Linux systems administration skills.
Experience with Slurm workload manager.
Experience using Pyxis and Enroot for containerized GPU workloads.
Strong scripting skills using Python and Bash.

Technical Expertise
AI Frameworks

PyTorch
CUDA
NCCL
Triton (preferred)
TensorRT-LLM (preferred)

Cluster Scheduling

Slurm
Pyxis
Enroot

GPU Networking
Strong understanding of:

InfiniBand
RoCE v2
RDMA
GPUDirect RDMA
GPUDirect Storage
UCX
MPI
Network topology optimization
Congestion control
QoS
ECN/PFC
High-speed Ethernet (200/400/800 GbE)

Storage
Experience designing or tuning storage for AI workloads, including:

Parallel file systems
Distributed storage
Object storage
NVMe
Checkpoint optimization
Dataset staging
GPUDirect Storage
Storage bandwidth optimization
Metadata performance

Performance Engineering
Experience with:

NCCL benchmarking
Multi-node scaling analysis
GPU utilization optimization
Communication/computation overlap
NUMA optimization
CPU affinity
PCIe topology
GPU topology (NVLink/NVSwitch)
Memory bandwidth analysis
End-to-end performance profiling

Preferred Qualifications

Experience deploying AI workloads on Kubernetes.
Experience with NVIDIA GPU Operator.
Experience with Kubernetes batch scheduling (Volcano, Kueue, Run:ai, etc.).
Experience with distributed inference platforms such as vLLM, TensorRT-LLM, or SGLang.
Experience with NVIDIA DGX SuperPOD or similar large-scale GPU deployments.
Familiarity with MLPerf benchmarking.
Experience deploying monitoring solutions such as Prometheus, Grafana, and DCGM Exporter.
Experience automating infrastructure using Ansible, Terraform, or similar tools.
Experience working in cloud GPU environments (AWS, Azure, GCP) in addition to bare metal.

Quick Apply

Refine Your Search

broadcast engineer jobs in san francisco bay area, ca

Cluster Engineer

Principal Software Engineer, WebRTC

IT Support Engineer, Executive Support

Software Dev Engineer II, Amazon Interactive Video Service (IVS)

Lead RF Engineer

AV Technician (5163C) Intercollegiate Athletics

Staff QA Engineer - Cinema Imaging

Staff Systems Engineer, Microsoft Teams

Music Coordinator (Part-Time)

Technical Engineer

Sr. RF Engineer

Mechanical Engineer II

QA Manager, Cinema Technology

Director, IT

Electronics Engineering Technologist

Senior Technical Program Manager, Creative Technology, Marketing

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