Machine Learning Engineer - Agentic AI

Benefits

Parental leave

Not verified

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

Not verified checked Jun 13, 2026

Salary

$147K-$272K From the posting source checked Jun 20, 2026

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

80% above the BLS role benchmark for software engineering aggregate.

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

Not verified

Weekend work

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Company

Equity

Offered Verified - SEC 10-K 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

Machine Learning Engineer - Agentic AI Sunnyvale, United States of America The VCV organization has pioneered human-centric, real-time technologies such as Face ID, FaceKit, and gaze and hand gesture control, transforming how millions of people interact with their devices by balancing research and product requirements to deliver Apple-quality experiences through full-stack innovation and close collaboration across HW, SW, and AI teams. Our team develops and integrates large language models (LLMs) and other cutting-edge AI techniques to enhance the autonomy and intelligence of our agentic systems. We are looking for an experienced engineer to design and build end-to-end agentic systems. This role focuses on developing systems that integrate large language models, tools, and data systems to execute multi-step workflows in production environments. You will work across the full stack to translate complex problems into reliable, scalable systems. You will design and implement agentic systems built around large language models (LLMs) that extend beyond traditional machine learning pipelines. The work will require making tradeoffs between latency, cost, accuracy, and controllability, including decisions between deterministic pipelines and adaptive, LLM-driven approaches within agentic system design. Build systems that combine models, tools, and data into cohesive, agentic workflows capable of executing multi-step tasks. This includes designing system behaviors such as planning, tool use, structured outputs, and failure handling. Develop infrastructure for evaluating and improving agentic system performance, including quality, reliability, and cost, and build monitoring and observability systems to understand behavior in production. Integrate LLMs with internal and external tools, enabling agentic systems to retrieve

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