Machine Learning Engineer (AIOps), WW CSO
Apple
Machine Learning Engineer (AIOps), WW CSO
Job Description
Imagine what you could do here. At Apple, great ideas have a way of becoming phenomenal products, services, and customer experiences very quickly. Bring passion and dedication to your job and there’s no telling what you could accomplish! Why Apple? At Apple, we believe our products begin with our people. By hiring a diverse team we drive creative thought. By giving that team everything they need we drive innovation. By hiring incredible engineers we drive precision. And through our collaborative process we build memorable experiences for our customers! We are looking for a highly skilled and experienced AIOps Machine Learning Engineer who has a robust understanding of Large Language Models and Generative AI to help work on exciting technologies for future Apple products and bring it to live. In this role, you will join a team of machine learning engineers with different specialization to discover and build solutions to previously-unsolved challenges and push the state of the art for global audience. You will collaborate with multi-functional teams of business SMEs, engineers, data scientists, designers, and researchers. This role is exceptionally technical, and will require you to actively engage in all aspects of the work, from conceptualization and theoretical considerations to design, coding, and implementation. In this role, you will focus on the following key areas: Model Management: Oversee the deployment, maintenance, and scaling of LLM, and services within our consumer-facing products. CI/CD: Build and maintain CI/CD pipelines to automate model train/test/deployment and scaling. Collaborative Integration: Partner with product developers, UX designers, and data scientists to ensure a seamless and intuitive integration of language models into our products. Continuous Monitoring: Build Dashboard and Regularly track model performance, ensuring consistent accuracy and reliability for consumers. Set up alerts and manage the type of monitoring needed. Feedback Integration: Develop strategies for collecting user feedback and refining the model for better alignment with consumer needs. Bias Mitigation: Proactively address and reduce potential biases in model predictions, ensuring our products are inclusive and fair. Infrastructure Management: Design and implement efficient data pipelines to support large language model training and inference Documentation: Maintain comprehensive documentation covering model versions, deployment protocols, and performance metrics. Research & Development: Stay updated with the latest trends in large language models and MLOps, ensuring our consumer products remain at the forefront of innovation.
Locations
- Singapore, Singapore, Singapore 569141
Salary
Estimated Salary Rangemedium confidence
50,000,000 - 120,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- understanding of Large Language Modelsintermediate
- understanding of Generative AIintermediate
- model managementintermediate
- deployment of LLMintermediate
- maintenance of LLMintermediate
- scaling of LLMintermediate
- building CI/CD pipelinesintermediate
- maintaining CI/CD pipelinesintermediate
- automating model train/test/deploymentintermediate
- collaborative integrationintermediate
- partnering with product developersintermediate
- partnering with UX designersintermediate
- partnering with data scientistsintermediate
- continuous monitoringintermediate
- building dashboardsintermediate
- tracking model performanceintermediate
- setting up alertsintermediate
- managing monitoringintermediate
- feedback integrationintermediate
- collecting user feedbackintermediate
- refining modelsintermediate
- bias mitigationintermediate
- addressing biasesintermediate
- reducing biasesintermediate
- infrastructure managementintermediate
- designing data pipelinesintermediate
- implementing data pipelinesintermediate
- supporting LLM trainingintermediate
- supporting LLM inferenceintermediate
- documentationintermediate
- maintaining comprehensive documentationintermediate
- research and developmentintermediate
- staying updated with LLM trendsintermediate
- staying updated with MLOps trendsintermediate
- codingintermediate
- implementationintermediate
- designintermediate
- theoretical considerationsintermediate
- conceptualizationintermediate
- collaboration with multi-functional teamsintermediate
Required Qualifications
- M.S. in related field with 3+ years experience applying AIOps machine learning engineer to real business problems. (experience, 3 years)
- Language Model Expertise: Demonstrated experience working with large language models, such as OpenAI's GPT series or similar. (experience)
- MLOps Skills: Solid background in Automating ML pipeline, including the training, testing, deployment, monitoring, and scaling of AI models. (experience)
- Technical Toolkit: Proficiency in PyTorch, TensorFlow, Transformers, Kubernetes, Docker, LangChain, vectorDB and cloud platforms like AWS, GCP, or Azure, and Monitoring tool like Grafana, and CI/CD like airflow, gitlab, and Big Data management like Spark, Kafka. (experience)
- Product-Centric Mindset: Proven track record of integrating AI models into consumer products, enhancing functionality, and user experience. (experience)
Preferred Qualifications
- Communication Prowess: Excellent communication skills, with the ability to liaise between technical and non-technical teams. (experience)
- Collaborative Spirit: Experience in cross-functional teams, ensuring seamless product development. (experience)
Responsibilities
- In this role, you will focus on the following key areas:
- Model Management: Oversee the deployment, maintenance, and scaling of LLM, and services within our consumer-facing products.
- CI/CD: Build and maintain CI/CD pipelines to automate model train/test/deployment and scaling.
- Collaborative Integration: Partner with product developers, UX designers, and data scientists to ensure a seamless and intuitive integration of language models into our products.
- Continuous Monitoring: Build Dashboard and Regularly track model performance, ensuring consistent accuracy and reliability for consumers. Set up alerts and manage the type of monitoring needed.
- Feedback Integration: Develop strategies for collecting user feedback and refining the model for better alignment with consumer needs.
- Bias Mitigation: Proactively address and reduce potential biases in model predictions, ensuring our products are inclusive and fair.
- Infrastructure Management: Design and implement efficient data pipelines to support large language model training and inference
- Documentation: Maintain comprehensive documentation covering model versions, deployment protocols, and performance metrics.
- Research & Development: Stay updated with the latest trends in large language models and MLOps, ensuring our consumer products remain at the forefront of innovation.
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Answer 10 quick questions to check your fit for Machine Learning Engineer (AIOps), WW CSO @ Apple.
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Machine Learning Engineer (AIOps), WW CSO
Apple
Machine Learning Engineer (AIOps), WW CSO
Job Description
Imagine what you could do here. At Apple, great ideas have a way of becoming phenomenal products, services, and customer experiences very quickly. Bring passion and dedication to your job and there’s no telling what you could accomplish! Why Apple? At Apple, we believe our products begin with our people. By hiring a diverse team we drive creative thought. By giving that team everything they need we drive innovation. By hiring incredible engineers we drive precision. And through our collaborative process we build memorable experiences for our customers! We are looking for a highly skilled and experienced AIOps Machine Learning Engineer who has a robust understanding of Large Language Models and Generative AI to help work on exciting technologies for future Apple products and bring it to live. In this role, you will join a team of machine learning engineers with different specialization to discover and build solutions to previously-unsolved challenges and push the state of the art for global audience. You will collaborate with multi-functional teams of business SMEs, engineers, data scientists, designers, and researchers. This role is exceptionally technical, and will require you to actively engage in all aspects of the work, from conceptualization and theoretical considerations to design, coding, and implementation. In this role, you will focus on the following key areas: Model Management: Oversee the deployment, maintenance, and scaling of LLM, and services within our consumer-facing products. CI/CD: Build and maintain CI/CD pipelines to automate model train/test/deployment and scaling. Collaborative Integration: Partner with product developers, UX designers, and data scientists to ensure a seamless and intuitive integration of language models into our products. Continuous Monitoring: Build Dashboard and Regularly track model performance, ensuring consistent accuracy and reliability for consumers. Set up alerts and manage the type of monitoring needed. Feedback Integration: Develop strategies for collecting user feedback and refining the model for better alignment with consumer needs. Bias Mitigation: Proactively address and reduce potential biases in model predictions, ensuring our products are inclusive and fair. Infrastructure Management: Design and implement efficient data pipelines to support large language model training and inference Documentation: Maintain comprehensive documentation covering model versions, deployment protocols, and performance metrics. Research & Development: Stay updated with the latest trends in large language models and MLOps, ensuring our consumer products remain at the forefront of innovation.
Locations
- Singapore, Singapore, Singapore 569141
Salary
Estimated Salary Rangemedium confidence
50,000,000 - 120,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- understanding of Large Language Modelsintermediate
- understanding of Generative AIintermediate
- model managementintermediate
- deployment of LLMintermediate
- maintenance of LLMintermediate
- scaling of LLMintermediate
- building CI/CD pipelinesintermediate
- maintaining CI/CD pipelinesintermediate
- automating model train/test/deploymentintermediate
- collaborative integrationintermediate
- partnering with product developersintermediate
- partnering with UX designersintermediate
- partnering with data scientistsintermediate
- continuous monitoringintermediate
- building dashboardsintermediate
- tracking model performanceintermediate
- setting up alertsintermediate
- managing monitoringintermediate
- feedback integrationintermediate
- collecting user feedbackintermediate
- refining modelsintermediate
- bias mitigationintermediate
- addressing biasesintermediate
- reducing biasesintermediate
- infrastructure managementintermediate
- designing data pipelinesintermediate
- implementing data pipelinesintermediate
- supporting LLM trainingintermediate
- supporting LLM inferenceintermediate
- documentationintermediate
- maintaining comprehensive documentationintermediate
- research and developmentintermediate
- staying updated with LLM trendsintermediate
- staying updated with MLOps trendsintermediate
- codingintermediate
- implementationintermediate
- designintermediate
- theoretical considerationsintermediate
- conceptualizationintermediate
- collaboration with multi-functional teamsintermediate
Required Qualifications
- M.S. in related field with 3+ years experience applying AIOps machine learning engineer to real business problems. (experience, 3 years)
- Language Model Expertise: Demonstrated experience working with large language models, such as OpenAI's GPT series or similar. (experience)
- MLOps Skills: Solid background in Automating ML pipeline, including the training, testing, deployment, monitoring, and scaling of AI models. (experience)
- Technical Toolkit: Proficiency in PyTorch, TensorFlow, Transformers, Kubernetes, Docker, LangChain, vectorDB and cloud platforms like AWS, GCP, or Azure, and Monitoring tool like Grafana, and CI/CD like airflow, gitlab, and Big Data management like Spark, Kafka. (experience)
- Product-Centric Mindset: Proven track record of integrating AI models into consumer products, enhancing functionality, and user experience. (experience)
Preferred Qualifications
- Communication Prowess: Excellent communication skills, with the ability to liaise between technical and non-technical teams. (experience)
- Collaborative Spirit: Experience in cross-functional teams, ensuring seamless product development. (experience)
Responsibilities
- In this role, you will focus on the following key areas:
- Model Management: Oversee the deployment, maintenance, and scaling of LLM, and services within our consumer-facing products.
- CI/CD: Build and maintain CI/CD pipelines to automate model train/test/deployment and scaling.
- Collaborative Integration: Partner with product developers, UX designers, and data scientists to ensure a seamless and intuitive integration of language models into our products.
- Continuous Monitoring: Build Dashboard and Regularly track model performance, ensuring consistent accuracy and reliability for consumers. Set up alerts and manage the type of monitoring needed.
- Feedback Integration: Develop strategies for collecting user feedback and refining the model for better alignment with consumer needs.
- Bias Mitigation: Proactively address and reduce potential biases in model predictions, ensuring our products are inclusive and fair.
- Infrastructure Management: Design and implement efficient data pipelines to support large language model training and inference
- Documentation: Maintain comprehensive documentation covering model versions, deployment protocols, and performance metrics.
- Research & Development: Stay updated with the latest trends in large language models and MLOps, ensuring our consumer products remain at the forefront of innovation.
Tags & Categories
Answer 10 quick questions to check your fit for Machine Learning Engineer (AIOps), WW CSO @ Apple.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs