Research, Mid-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, Mid-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 Mid-training sits at the seam between pre-training and post-training and is one of the highest-leverage points in the entire model pipeline. This is where raw base model capability is sharpened into something that can reason deeply, generalize reliably, and serve as the foundation that post-training builds on. You will own the late-stage training decisions that determine what our models are fundamentally capable of: data mix and quality uplift, annealing schedules, context length extension, capability injection across coding, math, and reasoning, and the synthetic data strategies that make all of it scale. This role does cross-cutting work across what is classically considered both pre-training and post-training. We don't distinguish between research and engineering; we expect both. WHAT YOU'LL ACCOMPLISH - Data Mix and Quality Uplift: Design and iterate on high-quality data mixtures for late-stage and annealing training runs. Develop principled methods for sourcing, filtering, and weighting data to sharpen model capabilities without degrading general performance. - Capability Injection: Drive targeted improvements in coding, mathematics, and long-horizon reasoning through

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