AIML - Sr Software Engineer, Machine Learning Platform Technologies
Apple
AIML - Sr Software Engineer, Machine Learning Platform Technologies
Job Description
Are you an open-source contributor passionate about building the next generation of cloud-native ML infrastructure? We're seeking a hands-on technical leader with deep expertise in Kubernetes, Crossplane, Golang/Rust, and agentic workflows to design and scale the platforms that power Apple's Siri, Search, and AI/ML ecosystems. If you've contributed to CNCF projects such as Crossplane, ArgoCD, or Kubernetes, and you're driven to build infrastructure for ML training and inference—including optimizing for performance, cost, and automation—this role is for you. You'll architect at Apple scale, developing intelligent, declarative, and self-managing infrastructure that enables billions of seamless user experiences. Our MLPT Cloud Infrastructure Team within Apple's AI/ML organization designs, builds, and scales the foundational systems that power Siri, Search, and next-generation ML workloads. We're reimagining how infrastructure is managed—through agentic, event-driven workflows, Crossplane compositions, and self-healing control planes—to deliver Model Context Protocol (MCP)–based infrastructure servers that integrate seamlessly with ML and data workflows. You'll work closely with AI/ML engineers, SREs, and platform teams to deliver infrastructure that is automated, observable, and efficient across Apple-scale hybrid and multi-cloud environments.
Locations
- Cupertino, California, United States 95014
Salary
Estimated Salary Rangemedium confidence
50,000,000 - 80,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- open-source contributorintermediate
- Kubernetesintermediate
- Crossplaneintermediate
- Golangintermediate
- Rustintermediate
- agentic workflowsintermediate
- CNCF projectsintermediate
- ArgoCDintermediate
- ML trainingintermediate
- ML inferenceintermediate
- performance optimizationintermediate
- cost optimizationintermediate
- automationintermediate
- architecting at scaleintermediate
- declarative infrastructureintermediate
- self-managing infrastructureintermediate
- agentic workflowsintermediate
- event-driven workflowsintermediate
- Crossplane compositionsintermediate
- self-healing control planesintermediate
- Model Context Protocol (MCP)intermediate
- hybrid cloud environmentsintermediate
- multi-cloud environmentsintermediate
- collaboration with AI/ML engineersintermediate
- collaboration with SREsintermediate
- collaboration with platform teamsintermediate
- automated infrastructureintermediate
- observable infrastructureintermediate
- efficient infrastructureintermediate
Required Qualifications
- BS/MS in Computer Science or related field (or equivalent practical experience). (experience)
- 5+ years of experience in distributed systems or cloud infrastructure engineering. (experience, 5 years)
- Strong programming experience in Golang and/or Rust; expertise in building controllers, operators, or automation systems. (experience)
- Deep understanding of Kubernetes internals, controller-runtime, and Crossplane composition frameworks. (experience)
- Experience with ArgoCD, Helm, and Infrastructure-as-Code (Terraform, Pulumi, or Crossplane). (experience)
- Hands-on experience with GitOps, declarative configuration, and reconciliation-driven workflows. (experience)
- Proven ability to design and operate infrastructure for ML training and inference, including performance tuning and GPU optimization. (experience)
- Experience leading technical teams, driving architecture decisions, and mentoring engineers. (experience)
- Strong grounding in cloud cost efficiency, performance profiling, and system-level debugging. (experience)
Preferred Qualifications
- 9+ years in cloud infrastructure, SRE, or distributed systems roles. (experience, 9 years)
- Active contributor to CNCF open-source projects (e.g., Kubernetes, Crossplane, ArgoCD, Envoy, Prometheus). (experience)
- Deep expertise in Kubernetes API machinery, custom resources (CRDs), and control plane development. (experience)
- Experience with Model Context Protocol (MCP)–based systems or contextual orchestration servers. (experience)
- Familiarity with AIOps or agentic AI workflows in production environments. (experience)
- Strong understanding of observability, telemetry, and distributed tracing (OpenTelemetry, Prometheus, Grafana). (experience)
- Proven experience building ML infrastructure platforms (training clusters, inference services, model registries). (experience)
- Excellent communication, technical writing, and cross-functional leadership skills. (experience)
Responsibilities
- Our MLPT Cloud Infrastructure Team within Apple's AI/ML organization designs, builds, and scales the foundational systems that power Siri, Search, and next-generation ML workloads.
- We're reimagining how infrastructure is managed—through agentic, event-driven workflows, Crossplane compositions, and self-healing control planes—to deliver Model Context Protocol (MCP)–based infrastructure servers that integrate seamlessly with ML and data workflows. You'll work closely with AI/ML engineers, SREs, and platform teams to deliver infrastructure that is automated, observable, and efficient across Apple-scale hybrid and multi-cloud environments.
- Architect and develop cloud-native, agentic infrastructure platforms supporting ML training, inference, and large-scale distributed systems.
- Lead and mentor engineers building Crossplane-based control planes, Kubernetes operators, and ArgoCD-driven GitOps automation.
- Design, build, and optimize Model Context Protocol (MCP) servers that manage and contextualize infrastructure and application state across environments.
- Contribute to and upstream improvements in open-source CNCF projects, representing Apple in the cloud-native community.
- Implement observability, governance, and automation frameworks to ensure performance, reliability, and compliance.
- Collaborate with AI/ML and infrastructure teams to integrate agentic orchestration workflows for self-service provisioning, ML pipeline management, and dynamic scaling.
- Drive best practices for GitOps, IaC, and Kubernetes cluster lifecycle automation at global scale.
- Ensure systems are resilient, secure, and optimized for cost and performance across on-prem and multi-cloud environments.
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AIML - Sr Software Engineer, Machine Learning Platform Technologies
Apple
AIML - Sr Software Engineer, Machine Learning Platform Technologies
Job Description
Are you an open-source contributor passionate about building the next generation of cloud-native ML infrastructure? We're seeking a hands-on technical leader with deep expertise in Kubernetes, Crossplane, Golang/Rust, and agentic workflows to design and scale the platforms that power Apple's Siri, Search, and AI/ML ecosystems. If you've contributed to CNCF projects such as Crossplane, ArgoCD, or Kubernetes, and you're driven to build infrastructure for ML training and inference—including optimizing for performance, cost, and automation—this role is for you. You'll architect at Apple scale, developing intelligent, declarative, and self-managing infrastructure that enables billions of seamless user experiences. Our MLPT Cloud Infrastructure Team within Apple's AI/ML organization designs, builds, and scales the foundational systems that power Siri, Search, and next-generation ML workloads. We're reimagining how infrastructure is managed—through agentic, event-driven workflows, Crossplane compositions, and self-healing control planes—to deliver Model Context Protocol (MCP)–based infrastructure servers that integrate seamlessly with ML and data workflows. You'll work closely with AI/ML engineers, SREs, and platform teams to deliver infrastructure that is automated, observable, and efficient across Apple-scale hybrid and multi-cloud environments.
Locations
- Cupertino, California, United States 95014
Salary
Estimated Salary Rangemedium confidence
50,000,000 - 80,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- open-source contributorintermediate
- Kubernetesintermediate
- Crossplaneintermediate
- Golangintermediate
- Rustintermediate
- agentic workflowsintermediate
- CNCF projectsintermediate
- ArgoCDintermediate
- ML trainingintermediate
- ML inferenceintermediate
- performance optimizationintermediate
- cost optimizationintermediate
- automationintermediate
- architecting at scaleintermediate
- declarative infrastructureintermediate
- self-managing infrastructureintermediate
- agentic workflowsintermediate
- event-driven workflowsintermediate
- Crossplane compositionsintermediate
- self-healing control planesintermediate
- Model Context Protocol (MCP)intermediate
- hybrid cloud environmentsintermediate
- multi-cloud environmentsintermediate
- collaboration with AI/ML engineersintermediate
- collaboration with SREsintermediate
- collaboration with platform teamsintermediate
- automated infrastructureintermediate
- observable infrastructureintermediate
- efficient infrastructureintermediate
Required Qualifications
- BS/MS in Computer Science or related field (or equivalent practical experience). (experience)
- 5+ years of experience in distributed systems or cloud infrastructure engineering. (experience, 5 years)
- Strong programming experience in Golang and/or Rust; expertise in building controllers, operators, or automation systems. (experience)
- Deep understanding of Kubernetes internals, controller-runtime, and Crossplane composition frameworks. (experience)
- Experience with ArgoCD, Helm, and Infrastructure-as-Code (Terraform, Pulumi, or Crossplane). (experience)
- Hands-on experience with GitOps, declarative configuration, and reconciliation-driven workflows. (experience)
- Proven ability to design and operate infrastructure for ML training and inference, including performance tuning and GPU optimization. (experience)
- Experience leading technical teams, driving architecture decisions, and mentoring engineers. (experience)
- Strong grounding in cloud cost efficiency, performance profiling, and system-level debugging. (experience)
Preferred Qualifications
- 9+ years in cloud infrastructure, SRE, or distributed systems roles. (experience, 9 years)
- Active contributor to CNCF open-source projects (e.g., Kubernetes, Crossplane, ArgoCD, Envoy, Prometheus). (experience)
- Deep expertise in Kubernetes API machinery, custom resources (CRDs), and control plane development. (experience)
- Experience with Model Context Protocol (MCP)–based systems or contextual orchestration servers. (experience)
- Familiarity with AIOps or agentic AI workflows in production environments. (experience)
- Strong understanding of observability, telemetry, and distributed tracing (OpenTelemetry, Prometheus, Grafana). (experience)
- Proven experience building ML infrastructure platforms (training clusters, inference services, model registries). (experience)
- Excellent communication, technical writing, and cross-functional leadership skills. (experience)
Responsibilities
- Our MLPT Cloud Infrastructure Team within Apple's AI/ML organization designs, builds, and scales the foundational systems that power Siri, Search, and next-generation ML workloads.
- We're reimagining how infrastructure is managed—through agentic, event-driven workflows, Crossplane compositions, and self-healing control planes—to deliver Model Context Protocol (MCP)–based infrastructure servers that integrate seamlessly with ML and data workflows. You'll work closely with AI/ML engineers, SREs, and platform teams to deliver infrastructure that is automated, observable, and efficient across Apple-scale hybrid and multi-cloud environments.
- Architect and develop cloud-native, agentic infrastructure platforms supporting ML training, inference, and large-scale distributed systems.
- Lead and mentor engineers building Crossplane-based control planes, Kubernetes operators, and ArgoCD-driven GitOps automation.
- Design, build, and optimize Model Context Protocol (MCP) servers that manage and contextualize infrastructure and application state across environments.
- Contribute to and upstream improvements in open-source CNCF projects, representing Apple in the cloud-native community.
- Implement observability, governance, and automation frameworks to ensure performance, reliability, and compliance.
- Collaborate with AI/ML and infrastructure teams to integrate agentic orchestration workflows for self-service provisioning, ML pipeline management, and dynamic scaling.
- Drive best practices for GitOps, IaC, and Kubernetes cluster lifecycle automation at global scale.
- Ensure systems are resilient, secure, and optimized for cost and performance across on-prem and multi-cloud environments.
Tags & Categories
Answer 10 quick questions to check your fit for AIML - Sr Software Engineer, Machine Learning Platform Technologies @ Apple.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs