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Senior Software Engineer - AI Observability - AI, Search & Knowledge Platform
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Apple
Cupertino, CA (In Person)
Full-Time
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Job Description
Do you want to build the future of AI enabled observability at Apple? We're looking for an experienced AI observability engineer to design and build AI observability solutions that power Apple Intelligence, Search, and AI infrastructure powering Apple's intelligent products. We're at the forefront of building AI-first observability services, blending AI, cloud-first engineering, and industry standards to deliver smart, scalable solutions. Your work will directly impact the experience of billions of users on their favorite Apple devices. If you are a seasoned principal or senior software engineer with a proven track record in building AI enabled observability solutions and have a deep passion for observability, AI, cloud-native technologies and large-scale distributed systems, we want to talk with you.
DescriptionWe're pioneering the next generation of AI-powered observability solutions. While we innovate to build new solutions, we also leverage industry-standard open-source technologies. In this role, you will collaborate with a team of engineers to lead the design and development of user-facing observability features for AIML products and infrastructure. You will also be responsible for providing technical guidance, sharing observability best practices and know-how, leveraging AI pipelines and mentoring the team to develop and deliver best-of-class features and a delightful user experience for all users. Preferred QualificationsKnowledge of current Gen AI research and techniques in the following areas: MCPs, RAG systems, Agentic AI (multi-agent orchestration, tool calling)Hands-on experience with agentic AI frameworks (e.g. LangGraph, AutoGen, CrewAI) for building multi-step reasoning and tool-using agentsDemonstrated experience in building observability systems for metrics, distributed tracing, logs, profiling and in building observability data collection using OpenTelemetryDemonstrated proficiency in AWS services such as EKS and native Kubernetes, storage such as S3, networking, database and observability servicesExperience with large scale observability visualization systems with knowledge of popular visualization tools like Grafana, DataDog, and ELKProficiency using cloud-native software development tools including coding, CI/CD and testing frameworksBuilding large-scale incident management, alert management and notification systemsActive open source project contributions is a plusMinimum Qualifications7+ years of experience in building ML pipelines, portable workflows and in model tuning to deploy ML and LLM models in production for customer-facing features7+ years software engineering experience and strong background in computer science: distributed systems, algorithms and data structures, APIs and highly-scalable, reliable systems and micro-servicesDemonstrated experience using LLM and ML models for AIOps and model observabilityDemonstrated experience using LLMs, ML frameworks i.e. TensorFlow, PyTorch and libraries like Scikit-learn, NumPy, LangChain, MLFlow, KubeFlowDemonstrated experience in delivering well-architected, reliable, highly-scalable cloud-native distributed systems for data management, observability or analytics servicesStrong software engineering experience in design, development and testing in cloud-native environmentsStrong coding skills in Python, Go, Javascript, JavaDemonstrated experience in building large-scale micro-services using public cloud infrastructure and/or \
DescriptionWe're pioneering the next generation of AI-powered observability solutions. While we innovate to build new solutions, we also leverage industry-standard open-source technologies. In this role, you will collaborate with a team of engineers to lead the design and development of user-facing observability features for AIML products and infrastructure. You will also be responsible for providing technical guidance, sharing observability best practices and know-how, leveraging AI pipelines and mentoring the team to develop and deliver best-of-class features and a delightful user experience for all users. Preferred QualificationsKnowledge of current Gen AI research and techniques in the following areas: MCPs, RAG systems, Agentic AI (multi-agent orchestration, tool calling)Hands-on experience with agentic AI frameworks (e.g. LangGraph, AutoGen, CrewAI) for building multi-step reasoning and tool-using agentsDemonstrated experience in building observability systems for metrics, distributed tracing, logs, profiling and in building observability data collection using OpenTelemetryDemonstrated proficiency in AWS services such as EKS and native Kubernetes, storage such as S3, networking, database and observability servicesExperience with large scale observability visualization systems with knowledge of popular visualization tools like Grafana, DataDog, and ELKProficiency using cloud-native software development tools including coding, CI/CD and testing frameworksBuilding large-scale incident management, alert management and notification systemsActive open source project contributions is a plusMinimum Qualifications7+ years of experience in building ML pipelines, portable workflows and in model tuning to deploy ML and LLM models in production for customer-facing features7+ years software engineering experience and strong background in computer science: distributed systems, algorithms and data structures, APIs and highly-scalable, reliable systems and micro-servicesDemonstrated experience using LLM and ML models for AIOps and model observabilityDemonstrated experience using LLMs, ML frameworks i.e. TensorFlow, PyTorch and libraries like Scikit-learn, NumPy, LangChain, MLFlow, KubeFlowDemonstrated experience in delivering well-architected, reliable, highly-scalable cloud-native distributed systems for data management, observability or analytics servicesStrong software engineering experience in design, development and testing in cloud-native environmentsStrong coding skills in Python, Go, Javascript, JavaDemonstrated experience in building large-scale micro-services using public cloud infrastructure and/or \