Apple

Principal ML Software Engineer for Apple's Creativity Apps team, focusing on building and optimizing on-device ML features for creative editing tools. The role involves architecting efficient execution capabilities, implementing ML algorithms on-device, and collaborating with scientists and designers to deliver performant, ML-based application features, with a strong emphasis on computer vision and iOS platform capabilities.

This role focuses on building AI systems and algorithms to automate crash debugging and root-cause analysis for firmware boot failures in cellular modem chips. The engineer will develop AI-powered engineering tools, LLM APIs, AI agents, and automated workflows, leveraging AI for intelligent automation in silicon bring-up and validation.

This role focuses on enabling Finance teams to transform from manual data preparers to AI-enabled decision partners. The Finance Data Scientist will guide teams through a maturity journey from automation to AI and predictive models, empowering them to leverage capabilities like data automation, BI, advanced analytics, ML, and AI agent creation within a governed framework. Responsibilities include partnering with leaders, developing roadmaps, educating teams on concepts like prompt engineering and agentic AI design, collaborating on solutions like automated workflows and finance-specific AI agents, and championing governance and AI frameworks.

Software Engineer to design and build intelligent tooling, automation, and AI-powered workflows using AI agents to improve engineering productivity and software quality across Apple's media technology stack. This role involves prototyping and productizing systems that leverage agentic coding workflows, AI-driven code analysis, and automation pipelines to streamline development processes, accelerate issue resolution, and enhance code quality at scale.

This role focuses on designing and developing scheduling and orchestration systems for large-scale TPU workloads in multi-region clusters, supporting foundation model training and inference. It involves distributed systems, cluster management, and performance optimization.

Develop and train deep learning models for image restoration and fusion applications to improve Apple's camera imaging pipeline, contributing to consumer products.

Engineering Manager for Wallet Identity Services at Apple, focusing on leading a team that builds and operates ML-powered services for liveness detection, face matching, and image quality. The role involves full lifecycle ownership of these services, from architecture and development to deployment and monitoring, with a strong emphasis on MLOps and scaling ML models in production.

This role focuses on building and deploying machine learning models to create intelligent search experiences, aiming to improve search relevance, ranking, and user satisfaction within Apple's consumer products. The engineer will be responsible for the end-to-end lifecycle of these models, from research to production.

Technical Program Manager to help shape the future of scalable machine learning infrastructure. Lead planning, execution, and cross-functional coordination across engineering teams to deliver robust, high-performance ML systems that operate across Apple’s cloud and distributed environments. Define and run processes that ensure the timely delivery of GPU, compute, and data infrastructure components that support large-scale ML workloads.

Engineering Program Manager for AI/ML and Generative AI features within Apple Media Products. This role involves driving the end-to-end delivery of large-scale, cross-functional ML/GenAI programs, from strategic planning and execution to managing timelines, resources, and cross-functional collaboration. The role requires understanding the full ML lifecycle and mitigating technical challenges to ensure successful product launches.

Staff AI Software Engineer for Siri Core Modeling at Apple, focusing on the on-device software platform for Apple Intelligence. The role involves leading design and development of features, implementing performant code, identifying architectural changes, optimizing performance, and developing software that interfaces with LLMs and on-device intelligence to enhance Siri capabilities. The position also emphasizes leveraging LLMs and AI assistants for engineering productivity.

This role focuses on building and maintaining production-grade software systems and ML models for location intelligence using sensor data. It involves the full ML lifecycle from problem formulation to production deployment, with a strong emphasis on signal processing, data infrastructure, and scaling ML to hundreds of millions of devices.

Engineering Program Manager to drive the execution of large-scale, machine learning-powered search products and infrastructure at Apple, focusing on improving user experience and quality for over a billion customers.

Engineering Project Manager responsible for the internationalization and global launch of AI-driven products, including Apple Intelligence. This role involves leading technical integration of generative AI and ML features across multiple languages and countries, managing international data generation, and driving model evaluation strategy for audio, vision, and language models.

Machine Learning Engineer focused on GenAI applications, delivering projects end-to-end from conceptualization to deployment. The role involves statistical analysis, business intelligence solutions, and presentations to executives. It requires experience with LLMs/LMMs, agents, agentic workflows, and RAG applications.

Senior Machine Learning Engineer at Apple focused on Search & Knowledge Platforms, developing next-generation Search and Question Answering systems for Apple products like Siri and Spotlight. The role involves designing, training, and deploying ML models for search relevance and ranking, leveraging large language models and large-scale data management.

Tech Lead Machine Learning Engineer for Finance Digital Transformation at Apple. The role focuses on building data foundations, services, and platforms for insights and automated decisions within the Finance organization, specifically for product cost. Responsibilities include technical leadership, operationalizing AI solutions from prototype to production, and instilling ML engineering practices. Requires experience in ML algorithms, generative AI, agentic solutions, MLOps/LLMOps, data pipelines, and Python.

Tech Lead Machine Learning Engineer for Finance at Apple, focusing on building and operationalizing AI solutions for product cost analysis and decision automation. The role involves leading engineers, collaborating cross-functionally, and bridging the gap between ML prototypes and production systems within the Finance organization. Requires experience in ML algorithms, generative AI, agentic solutions, MLOps, and scalable data pipelines.

This role focuses on designing, developing, and implementing sophisticated machine learning and AI models for creative applications, particularly image editing apps. It involves building end-to-end ML pipelines, prototyping AI features, developing AI tools, and deploying models into production. The role requires strong Python programming, experience with large-scale data, causal inference, LLM fine-tuning, RAG, and AI framework development.

The Machine Learning Test and Automation Engineer will design and build scalable test solutions for validating on-device and distributed Apple Intelligence inference, focusing on correctness, reliability, and performance of the inference runtime software. This role involves developing functional and performance tests within a CI/CD pipeline, partnering with ML engineering and infrastructure teams.

Machine Learning Research Engineer focused on image processing pipelines and innovative algorithms for Apple's mobile devices (iPhone, iPad). The role involves leveraging research in image processing, statistics, and computer vision to design, develop, and improve imaging technologies, turning concepts into commercially viable algorithms for Apple's camera systems.

MLOps Engineer role focused on building and optimizing ML infrastructure, ensuring reliability, scalability, and continuous improvement of AI/ML systems in production. Responsibilities include end-to-end quality initiatives, automated pipelines for training, evaluation, deployment, and championing model observability and governance.

ML Engineer focused on defining and analyzing quality metrics for AI-powered features in consumer productivity and creative applications. This role is critical for informing model development, feature launches, and product strategy by translating evaluation data and user behavior into actionable insights. It involves designing metrics frameworks, auditing data representativeness, and developing evaluation methods for complex, agentic AI experiences.

The role focuses on defining the architecture for systems that measure Siri's quality across platforms and model updates. It involves building evaluation infrastructure for large-scale automation, simulation, AI-powered auto-evaluators, and agentic fix pipelines. The Eval Systems Architect will own the technical vision and system architecture for Siri's evaluation stack, ensuring coherence, scalability, and trustworthiness, and will influence the technical roadmap for the evaluation platform.

Video Machine Learning Engineer at Apple, focusing on developing and integrating ML/DL solutions for video coding and processing in Apple products. Responsibilities include designing, training, optimizing, and integrating neural networks, as well as enhancing user experiences in video communication. Requires a Master's degree or equivalent experience in EE/CS, digital video processing, modern video codecs, and neural network design/optimization.

This role focuses on building and maintaining the Core ML Runtime for on-device execution of ML models on Apple products. The engineer will work on the ML graph compiler, runtime, and kernels, optimizing model execution for performance, energy efficiency, and thermal management. The role involves developing production-critical system software for implementing ML models on Apple Silicon, with a focus on common compiler optimizations and runtime systems.

Develop and ship computer vision, image processing, and machine learning software features for Apple's camera technologies, impacting millions of iPhone users.

This role focuses on designing, driving, and triaging automation pipelines and evaluation frameworks for Siri's AI features. The engineer will analyze large-scale test data, identify trends, and develop strategies to improve the efficiency and effectiveness of quality engineering processes. The goal is to ensure the qualitative experience of Siri's AI features meets high standards and to influence product decisions and model improvements.

This role focuses on building and automating backend tools for Siri's Speech data warehouses, including cataloging, annotation, and making the data queryable via LLM-based chatbots. It involves distributed data engineering at the intersection of speech recognition, NLP, and dialogue management, with the goal of improving training and evaluation of Siri's models.

Software Engineer role focused on the on-device platform for Siri's attention and invocation features, involving integration of ML models, performance optimization, and system-level architecture for multimodal machine learning solutions across Apple devices.

Software Engineer for Apple's Siri team, focusing on improving speech recognition, natural language understanding, and dialogue management through advanced statistical techniques and core machine learning algorithms. The role involves end-to-end feature development, collaboration with cross-functional teams, and shipping user-facing features to millions of users.

Senior Software Architect for Siri Attention and Invocation team, focusing on designing and implementing software systems for natural and conversational interactions with virtual assistants using AI/ML technologies, including LLMs and conversational AI. The role involves architecting ML-driven user experiences, integrating with sensory and system software, and optimizing on-device runtime performance.

Staff Machine Learning Performance Engineer for Siri, focusing on optimizing LLM and ML model inference stacks for performance and efficiency, involving on-device vs. server model decisions and collaboration with hardware/software teams.

Machine Learning Engineer at Apple to build next-generation features using multi-modal sensing, focusing on motion sensing and interactive technologies. Responsibilities include designing and implementing models & algorithms, optimizing for power, memory, and performance, developing data pipelines, and deploying efficient, low-power models for customer-facing products.

Data Engineer role focused on building and maintaining scalable data infrastructure for analytics, machine learning, and AI-driven decision-making within Operations. The role involves designing data pipelines, building data models, implementing data observability, and collaborating with data science and ML engineering teams. It also emphasizes leveraging AI-assisted tools for development and researching emerging GenAI data tooling.

Software Engineer focused on data collection, processing, and ML training data curation for on-device sensors and wireless technologies. The role involves building tools for data analysis, creating datasets for LLM benchmarking, and developing frameworks for evaluating generative AI output quality and reliability. It also includes designing data pipelines for sensor data and collaborating with teams to create intelligent experiences.

Software Engineer focused on building and managing internal security services that leverage AI technologies to identify and remediate security risks within the software development lifecycle. The role involves assessing AI-specific security risks and implementing controls, with a preference for experience in LLM-powered solutions.

Senior Engineering Manager to lead a team building foundational systems and services for Apple's AI and Data governance platform. The role focuses on Big Data management, ML infrastructure, and Generative AI, enabling efficient and scalable model development while ensuring regulatory compliance. It involves leading efforts in data lake governance and GenAI infrastructure across the entire model development lifecycle, influencing ML practitioners and product development.

Applied Scientist role at Apple focusing on causal inference, statistics, and machine learning to optimize marketing channels for consumer services. The role involves designing, developing, and deploying solutions using observational testing, counterfactual modeling, and lifetime value estimation, working with large-scale, privacy-focused datasets. Key responsibilities include engineering end-to-end causal inference products, analyzing data for automation and modeling opportunities, collaborating with cross-functional teams, and staying updated on research advancements.

This role focuses on building AI-assisted systems to analyze CI build and test failures, transforming raw data into actionable insights for OS engineers. The engineer will design and implement AI-powered workflows for failure summarization, pattern identification, and distinguishing signal from noise, integrating AI capabilities into developer tools to accelerate the software development process.

Machine Learning Engineer role focused on applying ML and optimization to supply chain and logistics problems for Apple's Fulfillment Operations team. The role involves designing, building, deploying, and maintaining end-to-end data pipelines and models to improve operational efficiency and security, with a focus on productionizing and owning the stack.

Machine Learning Engineer focused on Trust and Safety for communication apps, protecting against spam and abuse. The role involves building ML tooling for the entire ML lifecycle, from data ETL and model training to evaluation and deployment, with a strong emphasis on anti-fraud and anomaly detection, particularly in the context of LLMs. The goal is to deliver reusable tooling and integrate with existing systems to enhance customer safety and mitigate attack vectors.

This role is for an AI Solutions Engineer in Apple's Innovation Lab, focusing on helping enterprise customers build applications on Apple platforms using AI and Spatial Computing. The role involves consulting, architecting, building demonstrations, and evangelizing best practices for AI-powered enterprise applications. Requires expertise in LLMs, RAG, traditional ML, and Apple platform development.

This role focuses on applying AI/ML to Wi-Fi software for Apple products, optimizing performance, predicting congestion, and enhancing connection reliability. The engineer will design and implement ML models, apply AI to internal tools, analyze network data, and optimize models for on-device inference. The role involves collaboration with hardware and software teams, and a strong understanding of networking protocols and wireless standards is required.

Senior Software Engineer for Apple's Applied Sensing & Health team, focusing on delivering Health and Fitness features for Apple products. The role involves architecting and implementing C++ systems for sensor data processing, translating ML research into production algorithms, and maintaining system software with real-time constraints. Requires strong C++ skills, Python for ML, and understanding of ML pipelines.

This role focuses on building the ML infrastructure and developer experience for running ML models on Apple devices. It involves developing APIs for ML model conversion and authoring, optimizing models for efficiency and performance, and integrating ML tools into repositories. The goal is to enable efficient ingestion and implementation of models within Apple's ML stack, impacting various core experiences like Camera, Siri, and Health.

This role focuses on building and optimizing ML compilers and runtimes for on-device execution across Apple's diverse hardware (Neural Engine, GPU, CPU). It involves working with MLIR-based compiler stacks to improve runtime performance and enable efficient execution of ML models on Apple devices, impacting core experiences like Camera, Siri, and Health.

Seeking an experienced ML Infrastructure Engineer to build and optimize the execution engine and compilation toolchain for on-device ML models on Apple products. This role focuses on creating efficient, portable, and extensible runtimes and compilers, connecting compiler technology, runtime components, kernel libraries, and hardware compilers to enable ML execution across various devices.