Lab and Data Automation Engineer
Apple
Lab and Data Automation Engineer
Job Description
In this highly visible role, you will develop automation flows to store, analyze, and visualize Apple Silicon data, while also enabling instrument tracking and network automation for secure, scalable data collection. At Apple, we work every single day to craft products that enrich people’s lives. If you love working on challenges that no one has solved yet and automating tasks that span across multiple continents, we have the perfect opportunity for you. As a part of our extremely dynamic and forward-thinking group, you will have the rare and exciting opportunity to craft nuanced products that will surprise and delight millions of Apple’s customers every day. In this role, you will develop methods to improve and automate the collection, storage, processing, and visualization of silicon validation data from labs worldwide. You’ll build and deploy scalable data pipelines using scheduling systems and design infrastructure to support distributed validation across bare metal macOS, Docker, and Kubernetes environments. You will also automate the setup of silicon validation environments and manage data migration across systems. A part of the role includes instrument tracking and network automation—integrating with lab hardware to configure devices, manage network environments, monitor instrument status, and collect validation data securely and at scale. For efficient and insightful data analytics, you’ll build AI/ML based tools that accelerate data analysis and integrate with existing data analysis platforms. This includes tracking power utilization of lab equipment, identifying patterns in usage, and optimizing for future demand through predictive modeling. This work involves close collaboration with hardware, software, and infrastructure teams to enable rapid data exploration, debug large-scale systems, and implement alerting mechanisms that enhance observability and transparency across automation workflows.
Locations
- Cupertino, California, United States 95014
Salary
Estimated Salary Rangemedium confidence
25,000,000 - 45,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- develop automation flowsintermediate
- store dataintermediate
- analyze dataintermediate
- visualize dataintermediate
- instrument trackingintermediate
- network automationintermediate
- secure data collectionintermediate
- scalable data collectionintermediate
- improve data collectionintermediate
- automate data collectionintermediate
- data storageintermediate
- data processingintermediate
- data visualizationintermediate
- build scalable data pipelinesintermediate
- use scheduling systemsintermediate
- design infrastructureintermediate
- support distributed validationintermediate
- bare metal macOSintermediate
- Dockerintermediate
- Kubernetesintermediate
- automate setup of environmentsintermediate
- manage data migrationintermediate
- integrate with lab hardwareintermediate
- configure devicesintermediate
- manage network environmentsintermediate
- monitor instrument statusintermediate
- collect validation dataintermediate
- build AI/ML toolsintermediate
- accelerate data analysisintermediate
- integrate with data analysis platformsintermediate
- track power utilizationintermediate
- identify patterns in usageintermediate
- optimize for future demandintermediate
- predictive modelingintermediate
- collaboration with hardware teamsintermediate
- collaboration with software teamsintermediate
- collaboration with infrastructure teamsintermediate
- rapid data explorationintermediate
- debug large-scale systemsintermediate
- implement alerting mechanismsintermediate
- enhance observabilityintermediate
- enhance transparencyintermediate
- automation workflowsintermediate
Required Qualifications
- BS and 3+ years of relevant industry experience. (experience, 3 years)
Preferred Qualifications
- Proficiency in Python and best practices for automation and tooling. (experience)
- Experience building and maintaining CLIs, APIs, and end-to-end data pipelines using schedulers and orchestration tools. (experience)
- Familiarity with databases (SQL/NoSQL), file systems, and object storage. (experience)
- Hands-on experience with Docker, Kubernetes, CI/CD pipelines and version control systems. (experience)
- Proven track record to debug, deploy and support distributed systems in production. (experience)
- Experience automating network configuration and diagnostics to streamline lab setup and improve reliability. (experience)
- Experience designing secure data infrastructure with authentication, RBAC, and environment isolation. (experience)
- Proven collaborator across teams, emphasizing clarity, ownership, and timely communication. (experience)
- Experience working with scalable object storage and distributed file systems in cloud environments. (experience)
- Experience developing, deploying, and maintaining applied ML or statistical modeling pipelines to analyze lab and system data at scale. (experience)
- Experience implementing alerting and monitoring systems for workflow health and failure detection. (experience)
Responsibilities
- In this role, you will develop methods to improve and automate the collection, storage, processing, and visualization of silicon validation data from labs worldwide. You’ll build and deploy scalable data pipelines using scheduling systems and design infrastructure to support distributed validation across bare metal macOS, Docker, and Kubernetes environments.
- You will also automate the setup of silicon validation environments and manage data migration across systems. A part of the role includes instrument tracking and network automation—integrating with lab hardware to configure devices, manage network environments, monitor instrument status, and collect validation data securely and at scale.
- For efficient and insightful data analytics, you’ll build AI/ML based tools that accelerate data analysis and integrate with existing data analysis platforms. This includes tracking power utilization of lab equipment, identifying patterns in usage, and optimizing for future demand through predictive modeling.
- This work involves close collaboration with hardware, software, and infrastructure teams to enable rapid data exploration, debug large-scale systems, and implement alerting mechanisms that enhance observability and transparency across automation workflows.
Tags & Categories
Answer 10 quick questions to check your fit for Lab and Data Automation Engineer @ Apple.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs
Lab and Data Automation Engineer
Apple
Lab and Data Automation Engineer
Job Description
In this highly visible role, you will develop automation flows to store, analyze, and visualize Apple Silicon data, while also enabling instrument tracking and network automation for secure, scalable data collection. At Apple, we work every single day to craft products that enrich people’s lives. If you love working on challenges that no one has solved yet and automating tasks that span across multiple continents, we have the perfect opportunity for you. As a part of our extremely dynamic and forward-thinking group, you will have the rare and exciting opportunity to craft nuanced products that will surprise and delight millions of Apple’s customers every day. In this role, you will develop methods to improve and automate the collection, storage, processing, and visualization of silicon validation data from labs worldwide. You’ll build and deploy scalable data pipelines using scheduling systems and design infrastructure to support distributed validation across bare metal macOS, Docker, and Kubernetes environments. You will also automate the setup of silicon validation environments and manage data migration across systems. A part of the role includes instrument tracking and network automation—integrating with lab hardware to configure devices, manage network environments, monitor instrument status, and collect validation data securely and at scale. For efficient and insightful data analytics, you’ll build AI/ML based tools that accelerate data analysis and integrate with existing data analysis platforms. This includes tracking power utilization of lab equipment, identifying patterns in usage, and optimizing for future demand through predictive modeling. This work involves close collaboration with hardware, software, and infrastructure teams to enable rapid data exploration, debug large-scale systems, and implement alerting mechanisms that enhance observability and transparency across automation workflows.
Locations
- Cupertino, California, United States 95014
Salary
Estimated Salary Rangemedium confidence
25,000,000 - 45,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- develop automation flowsintermediate
- store dataintermediate
- analyze dataintermediate
- visualize dataintermediate
- instrument trackingintermediate
- network automationintermediate
- secure data collectionintermediate
- scalable data collectionintermediate
- improve data collectionintermediate
- automate data collectionintermediate
- data storageintermediate
- data processingintermediate
- data visualizationintermediate
- build scalable data pipelinesintermediate
- use scheduling systemsintermediate
- design infrastructureintermediate
- support distributed validationintermediate
- bare metal macOSintermediate
- Dockerintermediate
- Kubernetesintermediate
- automate setup of environmentsintermediate
- manage data migrationintermediate
- integrate with lab hardwareintermediate
- configure devicesintermediate
- manage network environmentsintermediate
- monitor instrument statusintermediate
- collect validation dataintermediate
- build AI/ML toolsintermediate
- accelerate data analysisintermediate
- integrate with data analysis platformsintermediate
- track power utilizationintermediate
- identify patterns in usageintermediate
- optimize for future demandintermediate
- predictive modelingintermediate
- collaboration with hardware teamsintermediate
- collaboration with software teamsintermediate
- collaboration with infrastructure teamsintermediate
- rapid data explorationintermediate
- debug large-scale systemsintermediate
- implement alerting mechanismsintermediate
- enhance observabilityintermediate
- enhance transparencyintermediate
- automation workflowsintermediate
Required Qualifications
- BS and 3+ years of relevant industry experience. (experience, 3 years)
Preferred Qualifications
- Proficiency in Python and best practices for automation and tooling. (experience)
- Experience building and maintaining CLIs, APIs, and end-to-end data pipelines using schedulers and orchestration tools. (experience)
- Familiarity with databases (SQL/NoSQL), file systems, and object storage. (experience)
- Hands-on experience with Docker, Kubernetes, CI/CD pipelines and version control systems. (experience)
- Proven track record to debug, deploy and support distributed systems in production. (experience)
- Experience automating network configuration and diagnostics to streamline lab setup and improve reliability. (experience)
- Experience designing secure data infrastructure with authentication, RBAC, and environment isolation. (experience)
- Proven collaborator across teams, emphasizing clarity, ownership, and timely communication. (experience)
- Experience working with scalable object storage and distributed file systems in cloud environments. (experience)
- Experience developing, deploying, and maintaining applied ML or statistical modeling pipelines to analyze lab and system data at scale. (experience)
- Experience implementing alerting and monitoring systems for workflow health and failure detection. (experience)
Responsibilities
- In this role, you will develop methods to improve and automate the collection, storage, processing, and visualization of silicon validation data from labs worldwide. You’ll build and deploy scalable data pipelines using scheduling systems and design infrastructure to support distributed validation across bare metal macOS, Docker, and Kubernetes environments.
- You will also automate the setup of silicon validation environments and manage data migration across systems. A part of the role includes instrument tracking and network automation—integrating with lab hardware to configure devices, manage network environments, monitor instrument status, and collect validation data securely and at scale.
- For efficient and insightful data analytics, you’ll build AI/ML based tools that accelerate data analysis and integrate with existing data analysis platforms. This includes tracking power utilization of lab equipment, identifying patterns in usage, and optimizing for future demand through predictive modeling.
- This work involves close collaboration with hardware, software, and infrastructure teams to enable rapid data exploration, debug large-scale systems, and implement alerting mechanisms that enhance observability and transparency across automation workflows.
Tags & Categories
Answer 10 quick questions to check your fit for Lab and Data Automation Engineer @ Apple.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs