AI Research Resident

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• 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)

$81,444 U.S. median for this role

Projected growth (BLS Employment Projections)

+6.9% - Faster than average

Matched to SOC 29-1141 - Healthcare aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Healthcare From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Work mode

Remote From the posting source checked Jun 20, 2026

In-office days

0 days From the posting source checked Jun 20, 2026

Schedule

Shift type

Not verified

Weekend work

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Company

Company stage

Seed From the posting source checked Jun 20, 2026

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

AI Research Resident San Francisco | Remote About Polymath Polymath is an applied research lab focused on advancing long-horizon agent capabilities through reinforcement learning. We design and scale simulation environments where agents learn to operate safely and autonomously. We work with the world's leading model labs to push the frontier of agent capabilities. Polymath is backed by Base10, Founders Future, Y Combinator, and other incredible investors & angels. We've raised an $8M seed, and are growing out the team. About the role We're looking for talented researchers currently enrolled in MS / PhD programs to collaborate on a research project focused around frontier benchmarks and environments for long-horizon AI agents. This will require 1) identifying failure modes in frontier models, 2) developing rigorous benchmarks that evaluate how well frontier agents perform on complex, realistic tasks requiring long-horizon reasoning and tool use in dynamic environments, and 3) training autonomous agents that can reason, plan, and act over extended time horizons. We can accommodate full-time or part-time engagements. The goal of the residency is to culminate in a publication, and if there is a mutual fit, transition into a full-time role. If you're interested in joining Polymath but are not currently a student, please apply to the Member of Technical Staff role. You'll be a good fit if you: - Are currently pursuing an MS or PhD program in Computer Science or a related field - Have experience with reinforcement learning, benchmarking frontier models, or model post-training - Have experience with systems

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