Staff Pay Product Manager, Generative AI

Posted 6 Days Ago
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San Francisco, CA
212K-254K Annually
3-5 Years Experience
Artificial Intelligence • Big Data • Machine Learning
The Data Platform for AI: High quality training and validation data for AI applications.
The Role
The Staff Pay Product Manager will build and enhance a pay system for experts, ensuring accurate and on-time compensation while supporting the workforce. Responsibilities include managing end-to-end product development, leading cross-functional teams, and executing product strategy with executive leaders.
Summary Generated by Built In

Scale is at the forefront of enabling Machine Learning across multiple industries by improving the world's leading Generative AI and Large Language Models.

 

We're looking for a Pay Product Manager to build and enhance our pay system, ensuring a robust, accurate, and on-time platform for our experts. This role is critical in maintaining the trust and reliability of our platform by incentivizing experts with fair, competitive pay that encourages high-quality and efficient work. You will work closely with cross-functional teams to develop solutions that safeguard our platform and grow our workforce.

 

This position reports to our Director of Product, Kate Park.

You will:

  • Experience building a system of records that handles money and operates under regulation.
  • Materialize a new profession with high-paying (often $40+/hour), flexible work for tens of thousands of people around the world.
  • Own end-to-end product development by understanding customer pain points, defining product requirements, managing development, testing, and launching products.
  • Lead cross-functional teams, including engineering, product design, operations, marketing, go-to-market, and finance.
  • Work with executive leaders to determine and execute the product strategy of the business.

Must be able to commute to the San Francisco office 3x weekly.

Ideally, you'd have:

  • Technical degree in computer science, engineering, or related field
  • Experience scaling and growth hacking consumer bases at hyper-growth startups
  • 6+ years of experience in product management or software development and extremely strong track record
  • Excellent communication and presentation skills
  • Excitement to work with AI technologies
  • Experience with products leveraging AI is a plus

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

The base salary range for this full-time position in the location of San Francisco is:

$212,000-$254,400 USD

PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.

About Us:

At Scale, we believe that the transition from traditional software to AI is one of the most important shifts of our time. Our mission is to make that happen faster across every industry, and our team is transforming how organizations build and deploy AI. Our products power the world's most advanced LLMs, generative models, and computer vision models. We are trusted by generative AI companies such as OpenAI, Meta, and Microsoft, government agencies like the U.S. Army and U.S. Air Force, and enterprises including GM and Accenture. We are expanding our team to accelerate the development of AI applications.

We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an affirmative action employer and inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status. 

We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at [email protected]. Please see the United States Department of Labor's Know Your Rights poster for additional information.

We comply with the United States Department of Labor's Pay Transparency provision

PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants' needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.

The Company
San Francisco, CA
523 Employees
On-site Workplace
Year Founded: 2016

What We Do

Scale accelerates the development of AI applications by helping machine learning teams generate high-quality ground truth data. Our advanced LiDAR, image, video and NLP annotation APIs allow machine learning teams at companies like OpenAI, Lyft, Pinterest, and Airbnb focus on building differentiated models vs. labeling data.

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