Research, Post-Training

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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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, Post-Training San Francisco | OnSite WHO WE ARE We are an applied AI lab building end-to-end software agents. We're the team behind Devin, the first AI software engineer, and Windsurf, an AI-native IDE. These products represent our vision for AI that doesn't just assist engineers, but works alongside them as a genuine teammate. Our team is small and talent-dense: world-class competitive programmers, former founders, and researchers from the frontier of AI, including Scale AI, Palantir, Cursor, Google DeepMind, and others. ROLE MISSION Post-training is the critical bridge between raw model capability and a system that is actually useful, safe, and effective in the real world. You will shape how our agents learn by iterating on training recipes, evaluations, and alignment methods that directly determine what Devin and our future systems can do. This role blends deep research and hands-on engineering. We don't distinguish between the two. WHAT YOU'LL ACCOMPLISH - Post-Training Recipe Development: Iterate on the full stack of datasets, training stages, and hyperparameters that determine model behavior. Measure how choices compound across evals and production performance, not just isolated benchmarks. - Evaluation Design and Integrity: Build evals that actually capture what matters. The loop never ends: define, optimize, realize the gaps, and rebuild. You'll be responsible for making numbers go up and making sure the numbers mean something. - Deep Understanding: When training produces results that don't make sense, you dig until you understand why. The goal isn't just to fix it; it's to carry that understanding forward

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