Staff/Sr. ML Compute Efficiency Engineer

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

$181K-$318K 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

114% 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

Staff Plus From the posting source checked Jun 20, 2026

Schedule

Shift type

Not verified

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

Staff/Sr. ML Compute Efficiency Engineer Santa Clara, United States of America Scaling machine learning workloads across thousands of GPUs and TPUs creates challenges that few engineers ever encounter. In Apple's Machine Learning Platform Technologies organization, we build the infrastructure that powers large-scale ML training and inference workloads, bringing together expertise in distributed systems, machine learning infrastructure, and high-performance computing. As a performance engineer in the ML Compute Efficiency team, you'll tackle ambiguous systems challenges, identify inefficiencies and build solutions that maximize accelerator utilization, reduce idle and fragmented capacity, and minimize recovery periods. This includes analyzing accelerator performance, digging into various parallelism techniques, and refining workload scheduling and orchestration across the compute fleet. Characterize ML workload behavior through profiling, benchmarks and metrics. Dive into unfamiliar codebases to prototype changes, evaluate tradeoffs, and build production-ready solutions. Design systems for efficient recovery from failures and preemptions. Create tools to identify and alert bottlenecks across applications and frameworks. Use workload-driven insights to influence next-generation hardware selection and procurement decisions. Collaborate closely with ML researchers and infrastructure engineers to address inefficiencies. Drive impact through hands-on contribution and mentorship. Minimum Qualifications: Experience with large-scale distributed systems for AI/ML workloads running on GPUs or TPUs. Strong software engineering skills with experience developing and optimizing training frameworks (e.g. PyTorch, JAX) using C/C++ or Python. Experience working on cross-functional projects with ML research and infrastructure teams. Familiarity with model architectures and various training techniques. Bachelor's degree in Computer Science or equivalent experience, with 7+ years of industry experience. Preferred

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