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Benchmarking Jobs in California (NOW HIRING)

General Motors

Applied ML Validation Manager

Sunnyvale, CA · On-site

$218K - $335K/yr

Develop and scale human benchmarking programs , including rater guidelines, calibration, and quality controls, to compare ML system performance against expert and typical human drivers. * Partner ...

General Motors

Applied ML Validation Manager

Sunnyvale, CA · On-site

$218K - $335K/yr

Develop and scale human benchmarking programs , including rater guidelines, calibration, and quality controls, to compare ML system performance against expert and typical human drivers. * Partner ...

ALO

Engineering Manager, Performance

San Ramon, CA · On-site

$188K - $235K/yr

Establish performance standards, KPIs, and benchmarking methodologies , including latency, throughput, error rates, and user experience metrics such as Core Web Vitals. * Guide performance ...

Nvidia

Client Platform Architect

Santa Clara, CA · On-site

You will deliver reference Blueprint specifications to product management, drive SW benchmarking process, align Power and Perf Benchmarking with business teams. Lead platform differentiation and ...

Advantest

Thermal Engineering R&D Intern

San Jose, CA · On-site

$19.75 - $25.50/hr

Validation & Benchmarking: Conduct simulation-based validation and performance benchmarking to compare algorithms, ensuring model accuracy against real-world measured system behavior. * Telemetry ...

ALO

Engineering Manager, Performance

San Ramon, CA · On-site

$188K - $235K/yr

Establish performance standards, KPIs, and benchmarking methodologies , including latency, throughput, error rates, and user experience metrics such as Core Web Vitals. * Guide performance ...

Rhoda AI

Robot System QA

Mountain View, CA · On-site

You'll design and execute the validation frameworks, benchmarking pipelines, and reliability testing programs that determine whether our systems are truly ready for real-world deployment. What You'll ...

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Benchmarking information

See California salary details

$50.3K

$79.3K

$112.5K

How much do benchmarking jobs pay per year?

As of Jun 30, 2026, the average yearly pay for benchmarking in California is $79,334.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,100.00 and $85,900.00 per year, depending on experience, location, and employer.

What is an example of a benchmark job?

A benchmark job is a standard position used for comparing compensation, skills, or performance across organizations. For example, a registered nurse or software engineer role often serves as a benchmark because they are common, well-defined, and have established salary ranges and job descriptions. These roles help organizations set pay scales and evaluate employee performance based on industry standards.

How does a Benchmarking Analyst typically collaborate with other departments to drive performance improvements?

Benchmarking Analysts frequently work cross-functionally, partnering with teams such as operations, finance, and quality assurance to collect data and compare organizational performance against industry standards. They facilitate workshops, share insights, and help identify actionable areas for improvement. This collaborative approach ensures that recommendations are tailored to each department's unique challenges and that initiatives are widely supported and successfully implemented.

What are the key skills and qualifications needed to thrive as a Benchmarking Analyst, and why are they important?

To thrive as a Benchmarking Analyst, you need strong analytical skills, attention to detail, and a background in business, statistics, or related fields. Familiarity with data analysis tools like Excel, SQL, or benchmarking software, as well as certifications such as Six Sigma, are often valuable. Excellent communication, critical thinking, and problem-solving abilities help you interpret data and present actionable insights to stakeholders. These skills are crucial for driving performance improvements and maintaining competitiveness by accurately comparing organizational practices against industry standards.

What jobs pay 2000 a day?

High-level consulting, executive coaching, and specialized freelance roles such as management consultants, financial advisors, or legal experts can earn around $2,000 per day. These positions typically require extensive experience, advanced skills, and often involve project-based or client-specific work. Compensation varies based on industry, location, and individual expertise.

What is the difference between Benchmarking vs Data Analyst?

AspectBenchmarkingData Analyst
Required credentialsOften requires business or industry-specific certifications, degrees in business, economics, or related fieldsTypically requires degrees in statistics, mathematics, or computer science; certifications like CAP or Microsoft Data Analyst
Work environmentPrimarily in corporate, manufacturing, or consulting settings focusing on performance comparisonIn various industries, working with data sets, reporting, and data visualization tools
Employer and industry usageUsed by organizations to improve processes by comparing against best practicesUsed across industries for data analysis, reporting, and decision-making support

While Benchmarking focuses on comparing organizational performance to industry standards, Data Analysts interpret data to inform business decisions. Both roles require analytical skills but serve different strategic purposes within organizations.

What is a benchmark job?

A benchmark job is a standard or reference position used by organizations to compare compensation, skills, and job requirements across similar roles. It helps in establishing pay scales and evaluating job market competitiveness, often requiring knowledge of industry standards and job analysis tools.

What is the best example of a benchmarked job?

A benchmarked job is one that has been compared against industry standards or best practices to determine appropriate compensation, skills, or performance levels. Examples include roles like software engineer or project manager, where salary ranges and responsibilities are often standardized through salary surveys and market analysis. Benchmarking helps organizations ensure competitive pay and effective role definitions.

What is benchmarking?

Benchmarking is the process of comparing a company's products, services, or processes against those of leading organizations in the industry or best practices from other industries. The goal is to identify areas where improvements can be made to increase efficiency, quality, or competitiveness. Benchmarking often involves collecting data, analyzing performance metrics, and implementing changes based on findings. This strategic approach helps organizations stay competitive and continuously improve their operations.
What are popular job titles related to Benchmarking jobs in California? For Benchmarking jobs in California, the most frequently searched job titles are:
What job categories do people searching Benchmarking jobs in California look for? The top searched job categories for Benchmarking jobs in California are:
What cities in California are hiring for Benchmarking jobs? Cities in California with the most Benchmarking job openings:
Benchmarking jobs near you
Staff Forward Deployed Engineer, AI/ML

Staff Forward Deployed Engineer, AI/ML

DigitalOcean

San Francisco, CA

Other

Posted 22 days ago

Job description

We are looking for a Staff Forward Deployed Engineer (FDE) who is passionate about operationalizing production AI-native and agentic workloads at scale. This is a high-impact role designed to serve as the "technical tip of the spear" for DigitalOcean's most strategic AI-native customers and platform initiatives.

As an FDE, you will operate at the intersection of Product Engineering, AI Infrastructure, and Customer Implementation. You will partner deeply with strategic AI-native enterprises (ANEs), startups, infrastructure vendors, and internal engineering teams to deploy, optimize, and scale production AI systems on DigitalOcean's AI-Native Cloud.

This role extends beyond traditional GPU infrastructure deployment. You will work across Inference Engine, runtime systems, orchestration frameworks, and AI-native applications to help customers operationalize production AI and agentic systems with strong focus on scalability, reliability, latency, and workload economics.

FDE engineers also act as the "first customer" for new AI-native platform capabilities. You will validate products under real-world workloads, surface operational insights and architectural gaps, and help accelerate product maturity through continuous feedback loops with Product Engineering and Research teams.

You will build scalable deployment frameworks, benchmarking systems, automation tooling, and AI starter kits that transform field learning into reusable platform intelligence and repeatable deployment patterns across the DigitalOcean ecosystem.

Your mission is to accelerate production adoption of AI-native systems while helping shape the future of DigitalOcean's AI-Native Cloud for the inference and agentic era.

What You'll Do
  • Strategic AI Workload Operationalization: Partner with strategic ANEs and AI startups to architect, deploy, optimize, and scale production AI and agentic systems on DigitalOcean's AI-Native Cloud. Support complex migrations, production-ready PoCs, deployment acceleration, and long-term workload expansion across inference and runtime platforms.
  • AI Performance & Systems Engineering: Optimize distributed inference and runtime performance through benchmarking, GPU efficiency tuning, KV-cache optimization, speculative decoding, prefill/decode disaggregation, multi-node deployments, and latency/cost optimization.
  • Platform Validation & Product Acceleration: Act as the "first customer" for DigitalOcean's AI-native platform capabilities including Inference Engine, runtimes, orchestration systems, GPU platforms, and deployment workflows. Surface real-world operational insights, architectural gaps, and scaling bottlenecks directly to Product Engineering and Research teams.
  • Platform Intelligence & Automation: Build scalable deployment assets including benchmarking systems, automation tooling, AI starter kits, deployment frameworks, operational playbooks, finetuning workflows, and reference architectures that improve deployment velocity and platform adoption.
  • Ecosystem & Technical Enablement: Collaborate with GPU vendors, model providers, infrastructure partners, and ISVs on co-development, technical validation, optimization, and launch readiness. Enable customer-facing technical teams and partner teams through validated deployment patterns, benchmarking insights, operational playbooks, reference architectures, demos, and technical guidance that help scale adoption of DigitalOcean's AI-native platform.
  • Travel: Ability to travel up to 30% for customer engagements, strategic onsite workshops, ecosystem partnerships, conferences, and internal collaboration.
Key Metrics
  • Customer Adoption & Production Success: Measured by high-impact production workloads launched, reduction in time-to-production, pilot-to-production conversion rates, and expansion of AI-native platform adoption across strategic customers.
  • Platform Intelligence & Product Influence: Measured by product improvements, roadmap influence, validated customer hypotheses, and operational insights generated from real-world production deployments.
  • Asset & Tooling Delivery: Measured through adoption of FDE-built frameworks, automation tooling, benchmarking systems, operational playbooks, and reference architectures across customers and internal teams.
  • Field Enablement & Ecosystem Scale: Measured through successful enablement of customer-facing teams, ecosystem collaboration outcomes, and adoption of FDE deployment standards across the AI-native ecosystem.
What You'll Add to DigitalOcean
  • AI-Native Systems & Architecture Expertise: Experience designing and operationalizing production AI systems including inference workloads, agentic runtimes, orchestration frameworks, and AI-native applications. Strong hands-on experience with inference and serving frameworks such as vLLM, SGLang, Ray Serve, NVIDIA Dynamo, llm-d, or equivalent systems, along with LLM optimization techniques including continuous batching, quantization, KV-cache optimization, and speculative decoding.
  • Distributed Systems & Infrastructure Mastery: Deep expertise with NVIDIA and AMD GPU platforms and their software ecosystems including CUDA, ROCm, TensorRT, Triton, NCCL, RCCL, NVLink, XGMI, and RoCE. Strong proficiency with Kubernetes (K8s), distributed systems, networking, storage systems, Infrastructure as Code, and large-scale AI infrastructure architectures.
  • Runtime & Orchestration Systems: Experience with AI orchestration and agent frameworks such as LangGraph, CrewAI, MCP ecosystems, LlamaIndex, OpenAI Agents SDK, or similar runtime systems. Understanding of workflow orchestration, deployment systems, memory patterns, and AI-native application architectures.
  • Software Engineering & Automation: Strong production coding skills in Python or Go with experience building tooling, automation systems, deployment workflows, benchmarking frameworks, and operational platforms.
  • Performance & Operational Intelligence: Proven ability to benchmark and optimize AI infrastructure with strong focus on scalability, reliability, GPU efficiency, runtime performance, latency optimization, and workload economics.
  • Consultative & Cross-Functional Execution: Ability to establish technical credibility with CTOs, Principal architects, Product Engineering teams, and ecosystem partners while managing high-impact production deployments and strategic technical initiatives.
Preferred Qualifications
  • AI Infrastructure & Forward Deployed Engineering Experience: Experience working in Forward Deployed Engineering, AI Infrastructure, Technical Consulting, AI Platform Engineering, or equivalent customer-facing engineering roles supporting production AI systems.
  • Platform Enablement & Ecosystem Experience: Experience building deployment standards, technical enablement programs, platform adoption frameworks, or ecosystem integration strategies across customer-facing and engineering organizations.
  • Open Source & AI Ecosystem Involvement: Active contributor to open-source AI, infrastructure, orchestration, or developer tooling ecosystems.
  • Vendor & Strategic Partnership Collaboration: Experience collaborating with GPU vendors, infrastructure providers, model vendors, or ecosystem partners on benchmarking, optimization, technical validation, or launch readiness initiatives.
Compensation Range: 
  • $195,000 - $239,000

*This is a remote role

JR: 2026-7748

#LI-Remote

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