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Remote Failure Analysis Engineer Jobs in San Rafael, CA

Everglade Works

Software Engineer

San Francisco, CA · Remote

$60 - $80/hr

Remote Software Engineer (USA Only) | $60 - $80 per hour Are you a software engineer who thrives in ... in a remote environment. * Excellent problem-solving skills and a strong analytical mindset.

Clera

Software Engineer, Applied AI

San Francisco, CA · On-site +1

$150K - $250K/yr

... and failure recovery. * Design and implement interfaces that make complex agent workflows clear ... Remote or hybrid arrangements are not available for this position. Visa sponsorship is not ...

GEI Consultants

DWR Project Engineer

Oakland, CA · On-site +1

$36.06 - $55.76/hr

Dam Safety Risk Assessment, Potential Failure Mode Analyses and Level 2 Risk Assessment * FERC ... Remote working is possible after an initial period. Assignments are typically 1-2 years, after ...

DEUNA

Fullstack Staff Engineer

San Francisco, CA · On-site +1

Guide teams on handling edge cases, failure scenarios, and observability. Technical Strategy ... Vacations and additional PTO Remote work from anywhere Economic support for health insurance ...

Cube Dev

Product Engineer

San Francisco, CA · On-site +1

As a Product Engineer at Cube, you'll take product ideas from spark to production -- owning both ... remote, async team. * You're fluent in English. Bonus points: * Experience building data, analytics ...

The Rockridge Group

Data Engineer

Emeryville, CA · Remote

$132K - $159K/yr

TITLE: DATA ENGINEER LOCATION: 100% remote Skillsets/experience: * Engineer background * Google Applications/Analytics experience required * Google Search console experience required * Google Tag ...

Mercor

Fraud Engineer - AI Specialist

San Francisco, CA · Remote

$80/hr

Remote Role Responsibilities * Use frontier AI coding agents to complete and evaluate complex fraud ... Identify bugs, edge cases, and failure modes to enhance model robustness. * Compare outputs from ...

Swish Analytics

Data Engineer

San Francisco, CA · Remote

$145K/yr

Company Overview Swish Analytics is a sports analytics, betting and fantasy startup building the ... This is a remote position. Duties * Support production systems and help triage issues during live ...

Swish Analytics

Data Engineer

San Francisco, CA · Remote

$145K/yr

Company Overview Swish Analytics is a sports analytics, betting and fantasy startup building the ... This is a remote position. Duties * Support production systems and help triage issues during live ...

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Showing results 41-60

All JobsFailure Analysis Engineer JobsRemote Failure Analysis Engineer JobsRemote Failure Analysis Engineer Jobs in San Rafael, CA

Remote Failure Analysis Engineer information

See San Rafael, CA salary details

$50.7K

$103.9K

$151K

How much do remote failure analysis engineer jobs pay per year?

As of Aug 6, 2026, the average yearly pay for remote failure analysis engineer in San Rafael, CA is $103,868.00, according to ZipRecruiter salary data. Most workers in this role earn between $78,000.00 and $131,500.00 per year, depending on experience, location, and employer.

What is the difference between Remote Failure Analysis Engineer vs Remote Quality Engineer?

AspectRemote Failure Analysis EngineerRemote Quality Engineer
CredentialsBachelor's in Engineering, certifications in failure analysis or reliabilityBachelor's in Engineering, certifications in quality management or Six Sigma
Work EnvironmentLaboratory, technical analysis, remote troubleshootingProcess audits, quality control, remote data analysis
Industry UsageElectronics, manufacturing, aerospaceManufacturing, electronics, automotive
Search & Comparison IntentUnderstanding failure analysis roles, technical skillsQuality assurance, process improvement roles

The Remote Failure Analysis Engineer focuses on diagnosing product failures through technical analysis, often involving lab work and remote troubleshooting. In contrast, the Remote Quality Engineer emphasizes maintaining quality standards, process improvements, and data analysis. Both roles require engineering backgrounds and certifications but serve different functions within manufacturing and electronics industries.

Remote Failure Analysis Engineer jobs near you
Infographic showing various Remote Failure Analysis Engineer job openings in San Rafael, CA as of August 2026, with employment types broken down into 82% Full Time, 14% Part Time, and 4% Contract. Highlights an 82% Physical, 4% Hybrid, and 14% Remote job distribution, with an average salary of $103,868 per year, or $49.9 per hour.

Senior Site Reliability Engineer

Andromeda Cluster, Inc

San Francisco, CA • On-site, Remote

$67.25 - $89.25/hr

Full-time

Re-posted 15 days ago

Job description

Senior Site Reliability Engineer
Location: Global Remote / San Francisco • Full-Time
About Andromeda
Andromeda Cluster was founded by Nat Friedman and Daniel Gross to give early-stage startups access to the kind of scaled AI infrastructure once reserved only for hyperscalers.
We began with a single managed cluster - but it filled almost instantly. Since then, we've been quietly building the systems, network, and orchestration layer that makes the world's AI infrastructure more accessible.
Today, Andromeda works with leading AI labs, data centers, and cloud providers to deliver compute when and where it's needed most. Our platform routes training and inference jobs across global supply, unlocking flexibility and efficiency in one of the fastest-growing markets on earth.
Our long-term vision is to build the liquidity layer for global AI compute - a marketplace that moves the infrastructure and workloads powering AGI not dissimilar to the flows of capital in the world's financial markets.
We are expanding to new frontiers to find the brightest that work in AI infrastructure, research and engineering.
The Role
This is not a generalist SRE role.
You will design, operate, and debug large-scale GPU infrastructure used for distributed training and inference, working directly with customers pushing the limits of modern AI systems.
We're looking for engineers who have personally run GPU clusters in production, understand the failure modes of distributed training, and can reason about performance from network fabric → kernel → framework.
What You'll Own
  • GPU Cluster Architecture: Design and evolve multi-provider, multi-region GPU compute clusters optimized for large-scale training. Make topology-aware scheduling, networking, and storage decisions that directly impact training throughput and cost efficiency.
  • Customer Technical Partnership: Serve as the primary technical point of contact for customers running large-scale training workloads. Onboard, troubleshoot, and optimize, often in real time.
  • Reliability & Performance Engineering: Define SLOs and error budgets that account for the unique failure modes of GPU infrastructure (ECC errors, NVLink degradation, NCCL timeouts). Own capacity planning across heterogeneous GPU fleets optimized for training throughput.
  • Networking & Fabric Health: Ensure the health and performance of high-speed interconnects (InfiniBand, RoCE, NVLink) that underpin distributed training. Diagnose and resolve fabric-level issues that degrade collective operations.
  • Observability: Build deep visibility into GPU utilization, memory pressure, interconnect throughput, training job performance, and hardware health. Go well beyond standard infrastructure metrics.
  • Automation & Tooling: Build production-grade automation for cluster provisioning, GPU health checks, job scheduling, self-healing, and firmware/driver lifecycle management.
  • Incident Leadership: Lead incident response for complex, multi-layer failures spanning hardware, networking, orchestration, and ML frameworks. Drive blameless postmortems and systemic fixes.

What We're Looking For
  • GPU Systems Expertise: Deep, hands-on experience operating large-scale GPU clusters (NVIDIA A100/H100/B200 or equivalent). You understand GPU memory hierarchies, ECC behavior, thermal throttling, and hardware failure modes from direct experience not documentation.
  • High-Performance Networking: Production experience with InfiniBand, RoCE, or NVLink fabrics in the context of distributed training. You can diagnose why an all-reduce is slow, identify a degraded link in a fat-tree topology, and reason about congestion control at scale.
  • Distributed Training & ML Frameworks: Working knowledge of how large training jobs actually run - NCCL, CUDA, PyTorch distributed, DeepSpeed, Megatron, FSDP, or similar. You don't need to write the models, but you need to understand what's happening at the systems level when a 1,000-GPU training run stalls.
  • Linux & Systems Internals: Expert-level Linux knowledge: kernel tuning, driver management (NVIDIA drivers, CUDA toolkit), cgroup/namespace internals, performance profiling at the syscall and hardware level.
  • Kubernetes & Orchestration: Strong experience running Kubernetes in production with GPU workloads, including device plugins, topology-aware scheduling, multi-cluster federation, and custom operators. Experience with Slurm or other HPC schedulers is equally valued.
  • Automation & Software Engineering: Strong engineering skills in Python, Go, or Bash. You build production-grade tools and services, not just scripts. Infrastructure-as-Code proficiency (Terraform, Helm, Ansible, or equivalent).
  • Observability & Monitoring: Hands-on experience building monitoring and alerting for GPU infrastructure, not just Prometheus/Grafana basics, but GPU-specific telemetry (DCGM, nvidia-smi, fabric manager metrics) integrated into actionable dashboards.
  • Incident Management: Proven track record leading incident response for complex distributed systems where the failure could be in hardware, firmware, networking, drivers, orchestration, or application code and you need to narrow it down fast.

Strong Candidates May Have
  • Distributed Storage: Experience with high-performance parallel file systems (VAST, Weka, Lustre, GPFS) and the checkpoint I/O and data-loading bottlenecks that come with large training runs.
  • Training Optimization: Experience profiling and optimizing distributed training performance: identifying stragglers, tuning collective communication strategies, improving MFU (Model FLOPs Utilization), and reducing idle GPU time across large runs.
  • Cluster Buildout & Hardware: Experience involved in physical cluster design - rack layout, power/cooling constraints, network topology design, and hardware validation/burn-in at scale.
  • Team Leadership: Experience leading or mentoring a team of infrastructure engineers. We're growing and need people who raise the bar for everyone around them.

Why You'll Love It Here
This is a high-impact, senior builder's role. You'll have significant ownership and autonomy to shape how our systems run at a foundational level, working directly with customers and providers while architecting the infrastructure backbone for reliable, scalable AI compute. You'll influence technical direction and help define what world-class AI infrastructure operations look like.
Andromeda Cluster is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

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