Research Scientist

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• 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)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

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

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Work mode

Hybrid From the posting source checked Jun 20, 2026

In-office days

2 days From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Research Scientist San Francisco | Hybrid | Remote Research Scientist / Machine Learning Scientist Location: SF Bay Area/Hybrid / Remote Type: Full-Time About the Role: The Client is seeking a variety of Machine Learning Scientist to help advance how we evaluate and understand AI models. You'll help design and analyze experiments that uncover what makes models useful, trustworthy and capable through human preference signals. Your work will contribute to the scientific foundations of understanding AI at scale. This role is deeply interdisciplinary. You'll work closely with engineers, product teams, marketing and the broader research community to develop new methods for comparing models, analyzing preference data, and disentangling performance factors like style, reasoning, and robustness. Your work will inform both the public leaderboard and the tools we provide to model developers. If you're excited by open-ended questions, rigorous evaluation, and research that's grounded in real-world impact, you'll find a meaningful home here. We're looking for: • Hands-on experience training large-scale models, including reward models, preference models, and fine-tuning LLMs with methods like RLHF, DPO, and contrastive learning. • Strong foundation in ML and statistics, with a track record of designing novel training objectives, evaluation schemes, or statistical frameworks to improve model reliability and alignment. • Fluent in the full experimental stack, from dataset design and large-batch training to rigorous evaluation and ablation, with an eye for what scales to production. • Deeply collaborative mindset, working closely with engineers to productionize research insights and iterating with product teams to align modeling goals

Read the full description at jobs.ashbyhq.com. FewerJobs shows a preview and links to the original posting.