Apple

Staff Machine Learning Engineer at Apple focused on Search and Knowledge Platform, developing next-generation Search and Question Answering systems using LLMs and RAG. Responsibilities include query understanding, retrieval, ranking, fine-tuning, and deploying models at scale, with a focus on production systems and online inference optimization.

Senior Computer Vision ML Engineer at Apple, focusing on fraud and identity protection for Apple Wallet using facial recognition and adversarial object detection. The role involves applied research and production deployment of computer vision models.

Machine Learning Scientist role focused on personalization for Apple's Media Products, involving research, development, and deployment of large-scale recommender systems for billions of users. The role requires expertise in deep learning, reinforcement learning, and generative AI applied to recommendations, with a strong emphasis on shipping production-quality code and contributing to scientific literature.

Machine Learning Tools Engineer for Apple's Global Siri team, focusing on building and improving LLM-powered intelligent assistant systems. The role involves end-to-end ML product engineering, from data generation and model training to deployment and evaluation, with an emphasis on scalable solutions and software engineering best practices.

This Applied AI Scientist role at Apple focuses on building and deploying scalable ML and AI solutions to enhance product intelligence and automation, particularly for creative applications like video editing. The role involves end-to-end ML pipeline development, prototyping AI features, and collaborating with engineering and product teams to bring intelligent solutions to production. It requires strong experience in statistical modeling, machine learning algorithms, large-scale data systems, and deploying models into production, with a preference for generative AI, computer vision, and LLM experience.

This role is for a Senior Machine Learning Engineering Manager for Ads Quality at Apple. The primary focus is on leading a team to build and deploy ML models that ensure the quality and integrity of the ad network, with a specific emphasis on using LLMs for relevance, safety, and trustworthiness. The role involves developing and applying advanced ML techniques in a large-scale production environment, working across product, policy, and engineering teams, and analyzing large-scale experiments.

Machine Learning Engineer specializing in Computer Vision for fraud detection within Apple's Strategic Data Solutions team. The role involves building end-to-end solutions, curating datasets, training models, and deploying them into a real-time platform to combat fraud, waste, and abuse.

Machine Learning Research Engineer focused on applying generative AI and ML to create innovative user experiences for hundreds of millions of users across Apple platforms. The role involves the full development cycle from ideation to productization, covering areas like handwriting and text recognition, synthesis, document understanding, and freeform drawing generation, with a focus on computer vision, speech recognition, deep learning, and multimodal LLMs.

ML Applied Scientist role focused on designing, developing, and deploying AI/ML solutions for Apple's services, including LLMs and Agentic AI, to enhance user content discovery. The role involves research, prototyping, production deployment, A/B testing, and collaboration with cross-functional teams.

Senior Machine Learning Engineer focused on Generative AI, responsible for converting high-level goals into concrete requirements, implementing, evaluating, and shipping AI/ML technologies for data quality or user-facing features. Requires experience in building large-scale ML systems, generative AI models, and working with large datasets, with a strong emphasis on shipping production-ready solutions.

This role focuses on designing, building, and deploying production-grade ML models for abuse detection across multiple modalities (text, image, audio) to protect Apple's ecosystem. It involves owning the full ML lifecycle, driving data strategy, architecting monitoring systems, and collaborating with cross-functional teams to deliver security solutions.

The role focuses on enhancing Siri and Apple products using generative AI and LLMs, with a strong emphasis on applied machine learning and software engineering. Key responsibilities include developing and improving generative experiences, creating evaluation techniques for model responses, and contributing to search relevance. The role also involves mentoring and collaborating with various teams.

Machine Learning Research Scientist for Apple Fitness+ personalization, focusing on developing and deploying state-of-the-art ML models for recommender systems at a global scale, with an emphasis on research and publication.

This role focuses on building and delivering state-of-the-art machine learning models for creative editing tools within Apple's consumer products. Responsibilities include data collection, model architecture design, training custom models, and partnering with cross-functional teams to integrate these features from concept to delivery. The ideal candidate has significant industry experience in computer vision and machine learning, with hands-on experience in building, training, evaluating, and deploying various model types, particularly vision models.

Software Engineer focused on building and productizing Generative AI experiences using NLP and ML for Apple platforms. The role involves research, development, and deployment of ML/NLP technologies, with a focus on on-device and private compute cloud applications.

Machine Learning Engineer on the Answers & Knowledge & Information team at Apple, focusing on improving Siri, Spotlight, and Safari search features. This role involves large-scale ML/DL for query understanding and ranking, using word embeddings, deep learning, and online learning on petabytes of data from millions of users. The engineer will design and build infrastructures, perform language and user intent analysis, process web-scale data, and run evaluations.

Senior Machine Learning Engineer for Apple Maps, focusing on improving search quality using ML and Generative AI. The role involves developing algorithms for query understanding, intent modeling, ranking, and semantic search, as well as building data pipelines and collaborating with other teams. Experience with LLMs, prompt engineering, and fine-tuning is required, along with strong programming and ML framework knowledge.

This role focuses on driving manufacturing research and development for iPhone final assembly line automation, integrating AI/ML and smart manufacturing technologies. The engineer will lead automation projects, leverage AI/ML and LLMs to optimize processes, and develop data-driven methods. Responsibilities include spearheading AI/ML adoption, leading core engineering projects, developing intelligent testing methods, conducting advanced data analysis, and mentoring teams on AI-augmented manufacturing.

The Senior Product Manager will define and deliver AI-powered solutions for live streaming moderation and VOC analytics, owning the product lifecycle and driving execution for AI systems that protect brand integrity and transform customer feedback into actionable business intelligence. This role requires a strong technical understanding of AI, familiarity with APAC social commerce, and the ability to translate business problems into product requirements, working with engineering teams to ship products that balance automation with human oversight.

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

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.

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.

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.

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.

Product Engineer for Apple Cloud AI Platform, focused on building end-to-end AI-driven systems and solutions for internal customers, translating real-world AI needs into production-ready platform offerings. This role involves working across the full stack, from data ingestion to model execution and UI integration, influencing platform roadmaps, and driving alignment between product management, partner platforms, and customer 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.

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

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.

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.

Develops software for next-generation fitness, safety, and health experiences by transforming multi-modal sensor data into insights using advanced machine learning and foundation models, impacting millions of Apple Watch, iPhone, and AirPods users.

Senior Machine Learning Engineer at Apple focused on building and optimizing the core auction system for their advertising platform. The role involves applying machine learning and auction theory to drive optimal outcomes for advertisers and users, working with large-scale systems and production code.

Senior Machine Learning Engineer at Apple focused on Ads Bidding & Pacing. The role involves designing and building scalable ML solutions for ad optimization, budget, and bid strategies to improve campaign performance and user experience. Requires experience in ML, quantitative optimization, production code development, and large-scale experimentation, ideally within the ads, recommendations, or search relevance domains.

Senior Machine Learning Engineer at Apple Ads focusing on building and optimizing core ML models for ad predictions, user interaction, click-through rate, and conversion rate. The role involves deep learning, information retrieval, large-scale modeling, and exploring LLMs and Reinforcement Learning for ad prediction systems across Apple's App Store and News platforms.

Research Scientist role focused on developing and deploying ML technologies for understanding, interacting with, evaluating, and generating user interfaces across Apple platforms. The work integrates into various Apple products and services, with a focus on shipping both internal tools and external products.

Staff Data Science Engineer for Siri's Attention and Invocation team, focusing on making Siri and Apple products more intelligent. The role involves leading ML solution development, establishing best practices for model validation and monitoring, driving technical decisions for ML/AI initiatives, and collaborating with stakeholders to define requirements and shape the data science roadmap.

Staff AI Software Engineer for Siri Core Modeling at Apple, focusing on the on-device software platform for Apple Intelligence. The role involves designing and developing features, implementing performant code, identifying architecture 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.

Staff Machine Learning Engineer for Apple Ads Platform, focusing on building and deploying ML systems and data pipelines to enhance advertiser trust and invalid traffic protections. The role involves defining an innovation roadmap, implementing robust CI/CD, feature stores, and streaming infrastructure, running A/B experiments, and optimizing performance for product quality, user experience, latency, and cost.

Develop and extend software features for iPhone camera, focusing on computer vision, image processing, and machine learning to deliver high-quality photos and videos. This role involves prototyping algorithms, working cross-functionally, and implementing solutions for new camera capabilities across Apple devices.

The Applied ML Science Manager will lead a team focused on Causal Inference and AIML solutions for Apple's services, impacting business decisions and marketing optimization. The role involves developing and deploying cutting-edge solutions, defining technical vision, and fostering a collaborative team culture. Responsibilities include leading scientists and engineers, overseeing technical strategy, managing projects, and collaborating with cross-functional teams. Requires a Master's degree, leadership experience, and expertise in Causal Inference techniques, ML, and programming languages.

This role focuses on applying machine learning and computer vision to industrial problems in manufacturing, specifically for inspection equipment. The engineer will develop and deploy ML models to judge millions of units daily, identify patterns and anomalies in data, and build toolkits for applying ML and statistical tools. Responsibilities include data analysis, software development in Python/Java/C++, and working with inspection systems. The role requires strong ML/CV experience in industrial settings, proficiency in Python and ML algorithms, and experience with image processing. It involves developing solutions from inception to production, with a focus on shipping AI-powered products for quality control in manufacturing.

Machine Learning Engineer role focused on improving search quality for Apple Maps using Generative AI and LLMs at scale. Requires experience delivering ML-driven products and strong programming skills.