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Home›Jobs›Anthropic›Incentive Compensation System Engineer
Anthropic

About Anthropic

Building safe and reliable AI systems for everyone

🏢 Tech👥 1001+ employees📅 Founded 2021📍 SoMa, San Francisco, CA💰 $29.3b⭐ 4.5
B2BArtificial IntelligenceDeep TechMachine LearningSaaS

Key Highlights

  • Headquartered in SoMa, San Francisco, CA
  • Raised $29.3 billion in funding, including $13 billion Series F
  • Over 1,000 employees focused on AI safety and research
  • Launched Claude, an AI chat assistant rivaling ChatGPT

Anthropic, headquartered in SoMa, San Francisco, is an AI safety and research company focused on developing reliable, interpretable, and steerable AI systems. With over 1,000 employees and backed by Google, Anthropic has raised $29.3 billion in funding, including a monumental Series F round of $13 b...

🎁 Benefits

Anthropic offers comprehensive health, dental, and vision insurance for employees and their dependents, along with inclusive fertility benefits via Ca...

🌟 Culture

Anthropic's culture is rooted in AI safety and reliability, with a focus on producing less harmful outputs compared to existing AI systems. The compan...

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Anthropic

Incentive Compensation System Engineer

Anthropic • San Francisco, CA | New York City, NY

Posted 3w agoMid-LevelOther technical roles📍 San francisco📍 New york
Apply Now →

Overview

Anthropic is seeking an Incentive Compensation System Engineer to lead the implementation and optimization of their ICM platform. You'll collaborate with various teams to ensure accurate compensation calculations and efficient workflows. This role requires strong technical skills and experience in system configuration.

Job Description

Who you are

You have a strong background in systems engineering, particularly in the context of incentive compensation management. Your experience includes leading technical implementations and configurations, ensuring that systems are scalable and efficient. You understand the importance of accurate calculations and reliable integrations, and you are adept at translating complex compensation plans into actionable system logic. You thrive in collaborative environments, working closely with cross-functional teams such as Revenue Operations, Data Engineering, Finance, Accounting, and HR. Your attention to detail ensures that all workflows are optimized for efficiency and accuracy.

Desirable

Experience with incentive compensation management platforms is a plus, as is familiarity with data integration and architecture. You are comfortable providing technical guidance during plan design discussions, advising on system capabilities and constraints. A background in finance or accounting can enhance your ability to understand the nuances of compensation structures and their implications on business operations.

What you'll do

In this role, you will lead the end-to-end implementation and configuration of the ICM platform, focusing on plan modeling, crediting rules, quota management, and payout calculations. You will translate compensation plan documents into system logic, ensuring that the outputs are accurate and auditable. Your responsibilities will include designing and maintaining system workflows for plan changes, exceptions, and adjustments, as well as developing and executing testing strategies to validate system accuracy before each compensation cycle. You will also provide technical guidance during plan design discussions, advising on system capabilities and constraints to ensure that the platform meets the needs of the organization.

What we offer

Anthropic offers competitive compensation and benefits, including optional equity donation matching, generous vacation and parental leave, and flexible working hours. You will have the opportunity to work in a collaborative office space in San Francisco, where you can engage with colleagues who are equally committed to building beneficial AI systems. We encourage you to apply even if your experience doesn't match every requirement, as we value diverse perspectives and backgrounds in our team.

Interested in this role?

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