Full Stack Software Engineer - ML Compute Capacity
Apple - Santa Clara, United States of America
Posted Feb 12, 2026
Benefits
- Parental leave
- Not verified
- Non-birth-parent leave
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- Family-building benefits
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- 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 last checked Jun 13, 2026
- Salary
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- 401(k) match
- Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.
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Schedule
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- Weekend work
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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
Full Stack Software Engineer - ML Compute Capacity Santa Clara, United States of America Scaling machine learning workloads across thousands of accelerators 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 senior engineer on the ML Compute Capacity team, you will design, build, and operate the production systems that ensure compute resources are optimally distributed throughout the company. You'll work across the stack - from data pipelines and backend services to APIs and interactive frontends - developing telemetry systems, optimization algorithms, policies, and intuitive tools for managing demand and improving efficiency across Apple's largest accelerator fleet. Our small, nimble team works in a high-autonomy, fast-paced environment, and we're passionate about digging into data patterns, laying out the performance characteristics of an entire distributed system, and knowledge sharing. If the opportunity to own and operate services that scale, stay highly available, and "just work" excites you, then please reach out to us! Build and operate demand and capacity planning systems Build data pipelines and telemetry systems that ingest, normalize, and serve fleet-wide utilization and cost data across multi-tenant and heterogeneous fleets Develop observability infrastructure - monitoring, alerting, and dashboards - that surfaces real-time fleet health and efficiency signals Drive innovation in forecasting, optimization, and supply chain management tooling that works at scale Build end-to-end tooling - from data models and
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