AI / Eval Engineer

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)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

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

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Work mode

Onsite From the posting source checked Jun 20, 2026

In-office days

5 days From the posting source checked Jun 20, 2026

Schedule

Shift type

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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 / Eval Engineer San Francisco | OnSite ABOUT VERA HEALTH Vera is the fastest-growing clinical app for healthcare professionals in the world. Five people built it. We're already operating at a scale most teams never reach, with a team most people would think is too small to pull it off. That's by design. Everyone here runs at a level that compresses what would normally take entire teams. We keep it that way by only hiring people who raise the standard the moment they join. We're building the founding team. The people who join now will define how this company operates for the next decade, and shape the future of healthcare. We move at a pace that feels unreasonable to most, and completely natural to the right people. If that pace feels natural to you, you belong here. THE ROLE You can't improve what you can't measure. Every answer on Vera is a clinical decision being made by a clinician. That's the bar. Not “mostly correct,” not “good on average.” We need to know exactly how the Vera behaves, where it holds up, where it breaks, and why. Think of the system like a chessboard with millions of different combinations. Different models, prompts, retrieval strategies, ranking, parameters, all interacting at once. Your job is to understand that space and push it in the right direction by optimizing every part of it, down to the smallest parameter. This isn't about writing eval scripts and calling it done. It's about going a

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