Apple

This role focuses on developing, carrying-out, interpreting, and communicating pre- and post-ship evaluations of the safety of Apple Intelligence features, leveraging both human and model-based auto-grading. It also involves researching and developing auto-grading methodology & infrastructure. The role requires creating safety evaluations that uphold Responsible AI values through data sampling, curation, annotation, auto-grading, and analysis. It draws on applied data science, scientific investigation, cross-functional communication, and metrics reporting.

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.

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.

Senior Staff Machine Learning Engineer at Apple Ads, focusing on innovation in ad prediction, quality, and privacy-preserving signals. The role involves building large-scale prediction models, designing privacy-respecting signals, and ensuring ad quality. It requires expertise in deep learning, prediction systems, and privacy-preserving ML, with a strong emphasis on production experience and technical leadership.

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 ML Engineer at Apple focused on building infrastructure and tools for the GenAI lifecycle, from experimentation to production inference, emphasizing observability, reproducibility, and modularity. The role involves contributing to architectural direction and designing for flexibility across future projects, with a strong focus on scalable data/compute infrastructure.

This role focuses on building scalable backend pipelines and services for training data used in Foundation Models and Apple Intelligence features. Responsibilities include converting raw data into training formats, processing and filtering large datasets, and developing APIs for data access. The role also involves large-scale inferences for pre-training and post-training using fine-tuned LLMs.

This role focuses on evaluating, optimizing, and analyzing the performance of ML and multi-modal LLMs. The Data Scientist will develop metrics, conduct failure analysis, process data for evaluation, and implement optimization techniques. They will collaborate with cross-functional teams to integrate models and communicate results. The role requires experience with model evaluation, RAG, and LLM prompt evaluation, with preferred experience in multi-modal foundation models and GenAI frameworks.

Senior Machine Learning Engineer in Finance at Apple, focused on building and extending AI/ML platforms and services for finance organizations. The role involves developing infrastructure for generative AI, agentic workflows, and ML inference, with a strong emphasis on MLOps, observability, and compliance within Finance governance frameworks.

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.

Senior Software Development Engineer in Test (SDET) for Apple Services Engineering's AI/ML quality organization, focusing on building developer tools, test frameworks, and libraries for ML pipelines and AI platform services. The role involves defining testing strategies, guiding tool selection, implementing automation, and collaborating with ML Engineers and Data Scientists to ensure quality and governance of ML outputs.

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 lead the software development of test systems for Apple products, and integrate AI/ML into daily workflows. This role involves designing and developing AI/ML solutions to improve workflows, team efficiency, and product quality, alongside developing and maintaining test libraries and integrating test systems.

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.

This role focuses on applying Generative AI and machine learning techniques to financial planning and analysis (FP&A) within the APAC region. The analyst will own core FP&A responsibilities, build intelligent data products to transform financial workflows, and leverage LLMs/GenAI frameworks for finance use cases. The role requires strong data fluency, experience with modern data science tools, and the ability to work with incomplete data to generate insights.

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.

This role focuses on developing and implementing next-generation automated processes for cosmetic and protective finishes (PVD coatings, Ink Printing) in high-volume manufacturing. It involves both physical automation (robotics, PLCs) and digital automation, including machine vision and deploying ML/AI models for predictive maintenance, defect detection, and process optimization. The role requires integrating automation parameters with materials chemistry and collaborating with cross-functional teams.

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.

This role is for an AI Solutions Lead responsible for designing and driving the delivery of enterprise-scale Generative AI and Data Platform solutions within Apple's enterprise. The role involves close collaboration with business stakeholders, hands-on prototyping, and guiding teams through execution to deliver impactful AI solutions.

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.

Language Engineer for Siri in Swedish, focusing on applying ML/NLP and LLMs to improve understanding and speaking of Swedish for Siri products. The role involves end-to-end user experience, data-driven analysis, implementing innovations, and working with LLM prompting and fine-tuning.

Language Engineer for Siri (Polish) responsible for end-to-end user experiences, applying ML and NLP techniques to deliver products, with a focus on developing, training, and fine-tuning LLMs for internationalization challenges in a large-scale consumer product.

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.