Machine Learning Architect for Conversational Speech at Apple, responsible for defining modeling strategy and technical direction for speech recognition, synthesis, dialog systems, multimodal foundation models, and speech-to-speech technologies. The role involves hands-on technical leadership, translating research into production-quality systems at scale, and ensuring architectural decisions align with on-device constraints, latency, and scalability.

Senior Machine Learning Engineer at Apple focused on innovating and applying state-of-the-art research in foundation models, particularly for audio data. The role involves the full ML pipeline from pre-training on large-scale unlabeled audio corpora to post-training evaluation and fine-tuning. Responsibilities include designing multi-modal data generation frameworks, building model evaluation pipelines, analyzing multi-modal data, and contributing to products with multi-modal perception data, especially audio and sensor fusion. The role also emphasizes representation learning, pre-training/fine-tuning for speech tasks, data selection techniques, and modeling data distributions. Collaboration with researchers and engineers is key, with opportunities for publishing groundbreaking research.

Applied AI Engineer at Apple Sales, focused on crafting and operating AI solutions using LLMs and agentic workflows for business problems. Responsibilities include designing agentic AI systems, translating research into production, building scalable pipelines, and leading technical decisions on infrastructure and safety mechanisms. Requires PhD or MS with significant experience in applied AI/ML, Python proficiency, and hands-on experience with LLMs, embeddings, vector databases, and agentic workflows.

Lead the E2E R&D and engineering for Generative AI models focused on summarization capabilities, including on-device and server-side LLMs, groundedness, and safety models. Develop inference frameworks and integrate with Apple's LLM infrastructure to deliver user experiences across various Apple products.

This role is for a Senior Director of Product Management and Marketing for AIML Technologies at Apple. The individual will set the vision, strategy, and execution for core AI technologies and experiences like Apple Intelligence and Siri, as well as developer and researcher platforms. Responsibilities include owning the strategy, definition, and launch of AI technologies, models, and experiences across the ecosystem, shaping Apple's AI platform strategy, and leading product management and marketing efforts from concept to global launch. The role requires deep expertise in AI, machine learning, and large language models, with a proven track record of executive-level leadership and influencing senior stakeholders.

The role focuses on applied research in responsible AI and safety for foundation models, including training, evaluation, alignment, and mitigations for deployment in Apple products. It involves collaboration with researchers and engineers to develop and deliver AI technologies that uphold Apple's values and privacy standards.

Machine Learning Engineer and Researcher to join the Creative Foundations team within Camera & Photos. This role involves inventing novel ML models at the intersection of research and product features, focusing on image understanding for consumer-facing applications. Responsibilities include designing architectures, training strategies, and intelligent systems, translating research into shippable features, and leveraging interpretability techniques. Requires MS/PhD, experience in ML/computer vision, proficiency in ML frameworks, and understanding of modern ML architectures. Preferred qualifications include a track record of creative problem-solving, published research, and specific computer vision experience.

Staff-level ML Security Research Scientist focused on applied research for production agentic ML systems, particularly tool-using models. The role involves leading research to identify and mitigate security vulnerabilities in these systems, designing realistic adversarial evaluations, and driving defenses into shipping products. The emphasis is on production impact and risk reduction, bridging research, platform engineering, and product security.

Senior Engineer to work on Generative AI for creative editing tools, focusing on computational photography and multi-modal image editing. Responsibilities include incubating ML algorithms, owning the model lifecycle (training to inference), designing data pipelines, and communicating research. Requires MS/PhD with 5+ years experience, deep ML knowledge (multimodal LLMs, MoE, PEFT, RLHF), and experience delivering customer-facing CV/GenAI products.

Machine Learning Systems Engineer for Siri, focusing on optimizing model training and inference for generative AI technologies on Apple Silicon. This role involves working across the ML stack, from training to deployment, to deliver production-level code for models impacting millions of users.

This role focuses on evaluating and improving AI systems by analyzing AI outputs, developing evaluation frameworks, and translating findings into actionable improvements. It involves assessing model behavior, identifying edge cases, and ensuring AI systems are reliable, safe, and aligned with human expectations. The role also involves building MLOps and automation for evaluation pipelines and collaborating with various teams to refine model performance.

Senior Engineering Manager to lead Applied AI experiences for Channel Sales, focusing on intelligent workflows, conversational experiences, and agentic AI capabilities for the end-to-end Commerce Journey. This role involves leading a team, driving product engineering strategy, and shipping ML-powered products and features, ideally involving LLMs and AI agents.

Apple is seeking a Machine Learning Researcher to conduct foundational research in LLMs and generative models, focusing on long-term, curiosity-driven projects. The role involves defining and executing research plans, implementing experiments, and publishing results in top-tier scientific venues. Collaboration with ML engineers and researchers across Apple is expected, with opportunities for technical mentorship.

This role focuses on researching, designing, and training state-of-the-art generative video models, primarily diffusion-based, with applications for creative users. It involves exploring novel architectures, spatiotemporal modeling, and multi-modal conditioning, aiming for real-world product impact.

Seeking a Senior Applied Researcher with expertise in Generative AI and LLM architectures to influence and implement foundational intelligence capabilities across Apple Services. The role involves architecting, designing, and deploying LLM-powered systems, leading research in areas like representation learning and RAG, driving LLM fine-tuning and evaluation, and exploring advanced methods like parameter-efficient adaptation and multi-agent orchestration. The goal is to build production-grade solutions that advance Apple's reasoning capabilities over text and behavioral signals at scale.

Senior Computer Vision and Machine Learning Engineer at Apple focused on Generative AI for creative editing tools. The role involves incubating, training, evaluating, and deploying ML models, particularly diffusion models, transformers, and GANs, with a focus on computational photography and multi-modal image editing. The engineer will also work on efficient inference and collaborate with cross-functional teams to bring these technologies to Apple products.

Senior ML Researcher in Foundation Models, Responsible AI. Focus on research and application of ML methods for breakthrough user experiences while upholding Apple's values, privacy, and quality standards. Will define and deliver responsible ML technologies, develop methods to train and evaluate foundation models with responsibility and safety in mind, research safety alignment and model robustness, and develop mitigations for safe LLM deployment.

Research Scientist role focused on fundamental research of foundation models for scientific domains, involving project definition, method development, experimental design, analysis, interpretation, publication, and applied problem-solving. Collaborates with internal teams.

Director of Algorithms for Apple Ads, leading applied scientists and ML engineers to build and scale intelligence for ads delivery. Focuses on retrieval, ranking, auction, and budget optimization systems at massive scale, balancing research innovation with operational excellence and privacy constraints. This role involves defining strategy, roadmap, and execution for ML systems that optimize advertiser outcomes and user relevance.

This role focuses on advancing Siri's conversational AI capabilities by designing, training, and evaluating machine learning models for production use cases. It involves building and maintaining scalable ML pipelines, optimizing models for performance, and contributing to ML infrastructure. The role requires experience across the full ML lifecycle, from data processing to deployment, with a focus on speech synthesis and recognition, natural language understanding, and dialog generation.

Staff Applied Scientist focused on AI Quality & Meta Evaluation, responsible for designing and building the Data Quality Validation framework for LLM Judges. This role involves developing statistical and ML approaches to ensure the trustworthiness of evaluation signals, auditing LLM outputs, and establishing standards for data quality.

This role focuses on building and scaling AI-native capabilities within Apple's iCloud data platform. The engineer will design, build, and own end-to-end AI systems, including agents, retrieval mechanisms, evaluation, guardrails, and observability. Key responsibilities involve optimizing AI systems for cost and performance, exploring state-of-the-art AI techniques, and educating the broader organization on AI patterns. The role requires experience in taking LLM or agentic systems from prototype to production, proficiency in modern AI frameworks, and a strong understanding of ML/DL principles.

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.

The role focuses on developing next-generation video codecs by applying machine learning, deep learning, neural video compression, generative AI, and computer vision. It involves contributing algorithms to industry standards and collaborating with software and hardware teams. The role emphasizes ML-driven approaches to video coding and processing.

Machine Learning Engineer on the Video Search team at Apple, focusing on building and deploying large-scale ML systems for search and discovery across Apple platforms. The role involves applying ML, NLP, and generative AI to model user intent, optimize retrieval and ranking systems, and integrate advanced ML technologies into production features used by millions. Key responsibilities include designing and implementing retrieval/ranking systems, building/deploying LLM models, analyzing performance, and collaborating with cross-functional teams.

Senior Engineer to lead GenAI and agentic systems for improving efficiency in large-scale cloud infrastructure operations. Focuses on building practical AI systems for engineering workflows, capacity planning, anomaly detection, and operational safety, using LLMs, RAG, and automation frameworks.

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 Research Engineer for Siri Speech team, focusing on creating and productizing ML algorithms for Speech Recognition, Speech Synthesis, and Conversational AI on Apple devices and cloud infrastructure. Requires expertise in compiled languages, ML frameworks, and applied ML, with the ability to translate research into user-facing products.

Research Engineer role focused on Computer Vision and Machine Learning for Apple Maps 3D Vision Team, developing novel methods for SLAM, 3D reconstruction, and localization using large, multi-modal datasets.

Engineering Manager for GenAI, AI & Data Platforms at Apple, leading a team to build scalable GenAI platform and applications, including RAG and Agentic components, with a focus on distributed systems, inference, and enterprise adoption.

Machine Learning Engineer at Apple responsible for designing, building, and deploying scalable, production-grade AI/ML systems, with a focus on LLM-powered features and AI agent workflows. This role involves end-to-end solution development, from concept to deployment, including integration into existing systems and continuous improvement of ML infrastructure.

Staff Machine Learning Engineer at Apple focused on building and scaling ML infrastructure and generative AI platforms. The role involves developing systems for ML data, embeddings, feature workflows, and enabling GenAI applications. Key responsibilities include designing scalable systems, improving experimentation and evaluation, optimizing inference and AI Ops, and prototyping/optimizing GenAI models for production. The role emphasizes end-to-end ML workflow experience, large-scale distributed systems, and modern generative techniques.

ML Engineer focused on building and scaling automated evaluation systems and designing adversarial/stress-testing methodologies for AI-powered features in productivity and creative applications. The role involves assessing AI quality, particularly for multi-turn agentic experiences, and influencing model development decisions through rigorous evaluation.

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.

Senior Applied Scientist focused on building and scaling AI evaluation and quality systems. The role involves developing methodologies, tooling, and autonomous QA agents to ensure the trustworthiness and quality of AI/ML systems, with a strong emphasis on human-in-the-loop evaluation and anomaly detection. Requires a blend of research and engineering skills to prototype, validate, and ship solutions.

Machine Learning Engineer on the Siri Speech team at Apple, focusing on designing, developing, and implementing ML models for speech, NLP, and multimodal applications. This role involves fine-tuning deep learning systems for speaker recognition and multimodal understanding, integrating ML solutions into production at scale, and working with large datasets to build production-quality models. The position requires strong Python skills, experience with ML algorithms and deep learning frameworks like TensorFlow/PyTorch, and knowledge of speech/audio processing.

Machine Learning Engineer for Siri's Attention & Invocation team, focusing on on-device voice invocation experiences. Responsibilities include building and deploying models, end-to-end training and evaluation pipelines, and optimizing speech/audio processing for Apple devices. Requires strong ML/DL background, speech recognition experience, and software engineering skills.

This role is a customer-facing AI/ML consulting engineer focused on positioning Apple's platforms and solutions within enterprise, education, and government sectors. The engineer will provide deep domain expertise on AI/ML solutions (cloud, hybrid, on-device/edge), articulate technical value propositions, advise on scalability, guide integration of AI/ML capabilities, and translate market trends into actionable guidance. The role requires extensive knowledge of Apple's AI/ML stack, including Apple Silicon, Foundation Models, Core ML, and Private Cloud Compute, as well as experience with major cloud AI/ML platforms.

Machine Learning Software Engineer at Apple focusing on spatial awareness and applied perception technologies. The role involves architecting and implementing production software systems for new ML technologies, particularly in computer vision, foundation models, and sensor-based perception. Key responsibilities include developing perception algorithms, optimizing ML training pipelines, fine-tuning vision transformers, and integrating ML with wireless and spatial sensors for iOS devices.

This role focuses on architecting and building distributed ML infrastructure for large-scale inference, specifically for powering Apple Intelligence. It involves designing parallelization strategies, optimizing the stack from low-level access to high-level algorithms, and collaborating with hardware architects. The goal is to achieve maximum hardware utilization and minimize latency for real-time user experiences, serving billions of requests daily.

This role focuses on building next-generation AI agents and tools for analyzing wireless connectivity and performance issues in Apple products. It involves developing advanced system evaluation and debugging capabilities using AIML tools, with an emphasis on improving user experience. The role requires expertise in ML frameworks, Gen AI techniques like RAG and Agent Orchestration, and Python scripting, with a strong understanding of wireless protocols.

Video Machine Learning Engineer at Apple, focusing on designing, developing, and deploying ML solutions for video processing, understanding, and enhancement. The role involves researching, prototyping, training, evaluating, and optimizing models for on-device deployment across various Apple products. It bridges deep learning research with real-world product impact.

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.

This role is for a Senior Machine Learning Engineering Manager at Apple, focusing on Ads Predictions. The manager will lead a team responsible for building and scaling complex ML models for response prediction (CTR, conversion rate) under latency constraints. Key responsibilities include driving the development and deployment of state-of-the-art models, owning the full ML lifecycle from data to production serving, and championing privacy-preserving ML approaches. The role requires strong leadership, cross-functional collaboration, and hands-on experience with large-scale ML systems, including neural networks and LLM-based systems.

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.