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Psl Scale For Modeling Jobs (NOW HIRING)

OpenAI

Performance Modeling Lead

Seattle, WA · On-site

$293K - $385K/yr

... for the unique demands of advanced AI workloads. We work closely with research, software, and ... Develop performance models to guide decisions on: * scale-up vs. scale-out architectures ...

Scale AI

Software Engineer, Platform

Manhattan, NY · On-site

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The ... models, and help enterprises and governments build, deploy, and oversee AI applications that ...

Family Care Network

Medical Assistant

Bellingham, WA · On-site

$23.48 - $34.83/hr

The full wage scale for a Medical Assistant (MA-C) is $23.48 - $34.83 per hour. Placement within ... PTO, PSL, and EIB accrue starting on your first day, and you can use paid time off after the ...

Family Care Network

Medical Assistant

Bellingham, WA · On-site

$23.48 - $34.83/hr

The full wage scale for a Medical Assistant (MA-C) is $23.48 - $34.83 per hour. Placement within ... PTO, PSL, and EIB accrue starting on your first day, and you can use paid time off after the ...

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All JobsPsl Scale For Modeling Jobs

Psl Scale For Modeling information

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$11K

$105.5K

How much do psl scale for modeling jobs pay per year?

As of Jun 19, 2026, the average yearly pay for psl scale for modeling in the United States is $97,166.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,000.00 and $105,000.00 per year, depending on experience, location, and employer.

What does PSL mean in modeling?

In modeling, PSL often refers to 'Pounds per Square Inch Load,' a measurement used to specify the load capacity or stress limits of materials or components. For jobs involving structural or mechanical modeling, understanding PSL helps ensure designs meet safety and performance standards. Knowledge of engineering principles and relevant tools like CAD software are typically required.

What is a good PSL score?

In the context of PSL Scale for Modeling, a good PSL score typically indicates strong performance and proficiency in modeling tasks, often reflecting high accuracy and reliability. While specific benchmarks vary by organization, scores above the median or a set threshold demonstrate competence and can improve job prospects in modeling roles. Developing skills in data analysis, software tools, and understanding modeling standards can help achieve higher PSL scores.

What is the PSL score model?

The PSL score model in modeling jobs typically refers to a performance or proficiency scoring system used to evaluate a candidate's skills, accuracy, and efficiency in performing modeling tasks. It helps employers assess a candidate's suitability based on quantitative metrics, often involving software tools and technical assessments. Understanding the PSL score can aid in preparing for evaluations and improving job performance.

How to find your PSL scale?

In PSL Scale for modeling, the scale typically refers to the ratio used to represent real-world objects accurately in models. To find your PSL scale, determine the size of your model compared to the actual object, often using standard ratios like 1:12 or 1:24, and adjust based on your project requirements. Using measurement tools and understanding scale conventions helps ensure consistency across your modeling work.
Infographic showing various Psl Scale For Modeling job openings in the United States as of June 2026, with employment types broken down into 80% Full Time, and 20% Part Time. Highlights an 97% Physical, 1% Hybrid, and 2% Remote job distribution, with an average salary of $97,166 per year, or $46.7 per hour.
Performance Modeling Lead

Performance Modeling Lead

OpenAI

Seattle, WA • On-site

$293K - $385K/yr

Full-time

Posted 29 days ago

Job description

About the Team
OpenAI's Hardware organization develops system and infrastructure solutions designed for the unique demands of advanced AI workloads. We work closely with research, software, and external hardware partners to shape the next generation of AI systems, from silicon through full-scale deployments.
Our team focuses on understanding and optimizing performance across the full system stack-ensuring that architectural decisions are grounded in rigorous, quantitative analysis of real-world workloads.
About the Role
We are seeking a Performance Modeling Lead to build and lead a small, high-impact team responsible for answering forward-looking architectural questions across AI infrastructure systems.
You will develop modeling frameworks and methodologies to evaluate system-level tradeoffs and guide key design decisions. Your work will directly influence reference architectures, vendor designs, and long-term infrastructure strategy.
This role sits at the intersection of AI workloads, system architecture, and quantitative modeling, and requires strong technical judgment, ownership, and the ability to translate complex analysis into clear, actionable guidance.
This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance.
Key Responsibilities
  • Build and own a performance modeling framework/toolchain to evaluate AI systems across multiple levels of abstraction.
  • Analyze and quantify architectural tradeoffs across compute, memory, networking, storage, and system topology.
  • Develop performance models to guide decisions on:
    • scale-up vs. scale-out architectures
    • interconnect and network design
    • memory hierarchy and system balance.
  • Translate modeling outputs into clear recommendations for internal teams and external hardware vendors.
  • Influence reference designs and vendor roadmaps through data-driven insights.
  • Partner closely with machine learning, systems, and hardware teams to understand workload characteristics and requirements.
  • Lead and grow a small team (2-3 engineers), setting technical direction and maintaining high standards for modeling rigor.
  • Continuously improve modeling fidelity by validating against real system behavior and measurements.

Qualifications
  • Have experience owning or building performance modeling frameworks used to drive real system design decisions.
  • Have deep knowledge of AI/ML workloads, including training and/or inference at scale.
  • Understand system-level tradeoffs across compute, memory, and networking in large-scale distributed systems.
  • Are comfortable working across abstraction layers-from workload behavior to hardware implementation.
  • Have experience using modeling (analytical or simulation) to inform architectural decisions.
  • Can operate in ambiguous problem spaces and turn open-ended questions into structured analysis.
  • Communicate clearly and influence both internal teams and external partners.

Preferred Skills
  • Experience working with hardware vendors (ODM/JDM, silicon, networking).
  • Background in data center infrastructure or hyperscale systems.
  • Familiarity with accelerators (GPUs/ASICs) and interconnects (e.g., NVLink, InfiniBand, Ethernet).
  • Experience influencing hardware roadmaps or reference architectures.
  • Prior experience leading or mentoring engineers.

About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
For additional information, please see OpenAI's Affirmative Action and Equal Employment Opportunity Policy Statement.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.
We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.
OpenAI Global Applicant Privacy Policy
At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

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