Staff Generative AI Research Engineer, Reasoning & Memory - SIML

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 last checked Jun 13, 2026

Salary

$181K-$318K not verified - source not recorded; timestamp not recorded

401(k) match

Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

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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

123% above the BLS role benchmark for data and ml aggregate.

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.

Schedule

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

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About this role

Staff Generative AI Research Engineer, Reasoning & Memory - SIML Cupertino, United States of America The System Intelligence and Machine Learning (SIML) Content Understanding teams are seeking a Staff Applied Researcher in Reasoning & Memory Systems. You will be working alonside teams that are in charge of operating system wide embeddings, personalized RAG workstreams, tool calling, context compaction / efficiency & memory systems. Projects are focussed on advancing Apple Intelligence capabilities, while working closely across disciplines with our partners in hardware engineering, design and product. Selected references to our prior work (a) https://arxiv.org/pdf/2507.13575, (b) https://arxiv.org/pdf/2407.21075, (c) https://www.apple.com/newsroom/2024/12/apple-intelligence-now-features-image-playground-genmoji-and-more/ We are seeking a candidate with a track record in algorithm development for agentic reasoning & memory. Key attributes expected in the role are fluency in algorithm development (prompt optimization and post training), strong expertise with relevant techniques (reinforcement learning, multimodal reasoning), and experience with automatic evaluation approaches for agentic workflow. The role includes the opportunity to partner with world class system engineers to prototype and incorporate bleeding edge algorithmic innovations in the context of emerging agentic experiences. Ability to interface with large scale modeling & data infrastructure is desired. Minimum Qualifications: PhD, or MSc in Computer Science/Electrical Engineering, or a related field (mathematics, physics or computer engineering); with a focus on machine learning, or comparable professional experience Strong ML and Generative Modeling fundamentals Strong expertise in one of the following: Reinforcement Learning, Multimodal Training, Pre-training / Post-training foundation models Proficiency in using ML toolkits, e.g., PyTorch Proven track record of research contributions

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