Research Product Manager (RPM)
Abundant - San Francisco | OnSite
Posted Jun 10, 2026
Benefits
- Parental leave
- Not verified
- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
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- Salary
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- 401(k) match
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Market context
- U.S. role benchmark (BLS OEWS)
- $102,662 U.S. median for this role
- Projected growth (BLS Employment Projections)
- +5.4% - Faster than average
Matched to SOC 11-1021 - Product Management aggregate by role bucket.
Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.
Role
Schedule
- Shift type
- Not verified
- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
Research Product Manager (RPM) San Francisco | OnSite ABOUT ABUNDANT As the need for scaling data becomes the core bottleneck to progress, moving from general knowledge to domain expertise, and from chatbots to agents. Abundant is solving this by designing and operating simulation infrastructure where next-generation models learn to reason and solve complex problems. Our team is composed of high-impact former founders, ML engineers, roboticists, and data leads from companies like Waymo and Google, with experience deploying models at 1B user scale. We partner with a majority of the top AI labs, frontier startups, and F500 enterprises to enable AGI, ASI, and physical intelligence. THE ROLE Research PM (RPM) is a role unique to Abundant. RPMs are PMs of the model, unlike traditional PMs who own an application or a feature. RPMs design and implement model capabilities, either as part of an AI lab, data lab, or other research organization. This is a founding-level role with outsized ownership. WHAT YOU'LL DO - Co-design capabilities of the model: define areas of improvement for the model, along with the Researcher. - Perform literature review: headroom analysis, identifying recent work in academic and industry circles. - Run experiments and benchmarks: using our internal simulation stack and our open source tooling such as Oddish to define and hillclimb on the vertical. - Become the voice of the model user: Talk to individual users and/or enterprises, in order to deeply understand where models and agents are underperforming, turning these insights into evaluation and training data.
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