AIML Researcher/Engineer - Foundation Model Post-Training

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

Not verified checked Jun 13, 2026

Salary

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401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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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

Schedule

Shift type

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Weekend work

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Company

Company stage

Public-company From the posting source checked Jun 20, 2026

Equity

Offered Verified - SEC 10-K source checked Jun 20, 2026

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

AIML Researcher/Engineer - Foundation Model Post-Training Cupertino, United States of America We are a tight-knit group of researchers and engineers responsible for building large scale frontier foundation models at Apple. We believe the most interesting breakthroughs in deep learning happen when we bridge the gap between raw model capability and user-centric utility. In this role, you will play a critical role shaping the future of our LLM efforts, specifically in transforming our models into highly capable, intelligent assistants that power billions of Apple products. You will tackle core training challenges in instruction following, tool use, deep reasoning, and architectural adaption - designing models that deliver magical, deeply integrated, and privacy-forward experiences across the Apple ecosystem. You will work alongside a fast-growing team of world-class experts to explore novel training strategies, architectural adaptations, and advanced evaluation methodologies. Design and iterate on end-to-end post-training strategies (including Reinforcement Learning) to unlock model capacities toward achieving specific model behaviors. Pioneer novel algorithms for preference optimization, model steering, and safety. Drive our data strategy by researching methods for high-quality human and synthetic data generation, automated data filtering, and curriculum learning to improve instruction following and reasoning. Design robust evaluation methodologies to measure model helpfulness, factuality, and utility, moving beyond static benchmarks to accurately capture real-world performance. Partner closely with pre-training teams to inform architecture choices, and with product teams to translate user requirements into model capabilities. Minimum Qualifications: Demonstrated expertise in deep learning with a focus on LLMs, post-training, or reinforcement learning, backed by a

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