Senior ML Infrastructure Engineer - VE Algorithms

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

Not verified checked Jun 13, 2026

Salary

$140K-$258K 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

71% 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

Senior 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

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Senior ML Infrastructure Engineer - VE Algorithms San Diego, United States of America Are you passionate about groundbreaking modeling technologies to enrich billions of people? We are the Video Engineering (VE) team at Apple developing cutting-edge video and machine learning algorithms to build the photo and video features that Apple devices are well known for. We are seeking engineers experienced in building infrastructure for training, adapting and deploying large-scale generative models. In this role, you will be working closely with a cross functional team of algorithm design and infrastructure engineers to benchmark, prototype and steer algorithmic choices to best fit our training and deployment infrastructure. In this role you will be technically hands on, with deep subject matter expertise in ML infrastructure, focusing on distributed and parallelized training of images and videos, and efficient utilization of training hardware. Optimize and profile training pipelines for large-scale visual pre-training across diffusion and auto-regressive architectures. Design and implement workload scheduling strategies for distributed training across clusters of thousands of GPUs. Profile and optimize across the full compilation stack - from high level graph capture through intermediate representations down to target HW. Build and maintain experimentation tooling, dashboards, and scalable CI/CD pipelines tailored to multi-GPU training workflows. Minimum Qualifications: BS in Electrical Engineering/Computer Science or a related field, with a focus on machine learning and minimum 3 years industry experience. Experience in training and adapting LLMs. Advanced fluency in PyTorch. Excellent programming skills in Python or C++ and experience contributing software to large projects.

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