AI Product Manager, Insights

Reposted 25 Days Ago
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San Francisco, CA, USA
In-Office
206K-257K Annually
Senior level
Artificial Intelligence • Big Data • Machine Learning
The Data Platform for AI: High quality training and validation data for AI applications.
The Role
The AI Product Manager will lead model evaluation analysis and insight generation, transforming raw data into actionable strategies for model improvement. Responsibilities include creating evaluation processes, analyzing model failures, and structuring findings for effective communication.
Summary Generated by Built In
The Role

We are looking for a highly analytical and strategic thinker to take ownership of our model evaluation analysis and insight generation. Our analysis has established a high standard for deep-dive analysis of model evaluation. We need someone who can not only maintain this cadence but elevate it, turning raw result data into a roadmap for model improvement.

Responsibilities
  • Own the creation of model evaluation from initial hypothesis, data scraping to final publication. 
  • Go beyond aggregate metrics (e.g., "Accuracy is 85%"). deeply analyze why the model failed on the other 15%. Identify semantic patterns, edge cases, and systemic hallucinations in raw model outputs.
  • Review raw data sets, meeting transcripts, and research notes to identify the "so what?" We need to turn these findings into a logical hierarchy 
  • You will act as the bridge between the data and the narrative by structuring findings into a logical hierarchy where the most critical "hook" lands first, followed by the supporting evidence
Who You Are (Requirements)
  • Experience: You have 5 - 10 years of experience in DS, ML, AI research and analysis
  • Structured Thinker: You organize your writing logically.
  • High Tolerance for Ambiguity: You can take a messy pile of notes and organize it into a coherent outline without needing your hand held.
  • Executive Presence: You are comfortable interviewing senior leaders and pushing back when an "insight" isn't actually insightful.
  • Cross Functionality: Be able to work cross functionally across ML researchers to clients.
Nice to Have
  • Experience in Model Evaluation, ML Engineering or Technical Research.
  • Experience designing or curating datasets (RLHF, SFT data)

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.

Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:
$205,600$257,000 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, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. 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 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.

Top Skills

Data Science
Machine Learning
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The Company
San Francisco, CA
523 Employees
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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