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Systems Engineer - Evaluation Engineering

Apple - Cupertino, United States of America

Posted Jun 9, 2026

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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Deadline
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Where they hire

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About this role

Systems Engineer - Evaluation Engineering Cupertino, United States of America We are looking for a Distributed Systems Engineer to own the infrastructure powering our core Siri Agentic Evaluation Platform. Evaluation is no longer just a static test suite-it is a highly dynamic, massive-scale distributed problem. Our platform enables teams to run high-throughput agentic simulations, orchestrate multi-model judging pipelines, and generate real-time observability dashboards across billions of tokens and complex data types. In this role, you will design the execution engine that coordinates these complex evaluation loops. You will build systems that remain deterministic, fault-tolerant, and cost-efficient, even when coordinating massive parallel requests across heterogeneous device types(iPhones, Mac, iPads etc). Distributed Execution Engine: Architect and scale the core asynchronous engine responsible for orchestrating thousands of parallel agent simulations, validation tests, and LLM-as-a-judge pipelines. Internal Developer Platform (IDP): Design and build self-service infrastructure, CLI tools, and internal APIs that allow ML and product teams to easily integrate evaluation pipelines into their CI/CD workflows. Backend API & Service Architecture: Design, build, and maintain highly performant, type-safe APIs (gRPC/REST) capable of serving complex evaluation pipelinee, trace data, and real-time generation metrics. Stream Processing & Lineage: Build robust data pipelines to ingest and transform high-volume execution traces. Ensure immutable data lineage so that every evaluation metric can be perfectly traced back to its raw generation for granular error attribution. Infrastructure-as-Code & GitOps: Own the deployment topologies of the evaluation platform across multi-tenant clusters using declarative infrastructure and continuous delivery practices. Reliability, Observability & Guardrails: Implement

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