Senior Generative AI Research Engineer, Multimodal, Agent Modeling - SIML
Apple
Senior Generative AI Research Engineer, Multimodal, Agent Modeling - SIML
Job Description
Are you passionate about Generative AI? Are you interested in working on groundbreaking generative modeling technologies to enrich billions of people? We are driving multiple initiatives focused on advancing generative models, and we are seeking technical leaders experienced in training, adapting and deploying large-scale generative models. This role emphasizes AI safety, multimodal understanding and generation, and the development of agentic systems that push the boundaries of what AI can achieve responsibly. We are the Intelligence System Experience (ISE) team within Apple’s software organization. The team operates at the intersection of multimodal machine learning and system experiences. It oversees a range of experiences such as System Experience (Springboard, Settings), Image Generation, Genmoji, Writing tools, Keyboards, Pencil & Paper, Generative Shortcuts - all powered by production scale ML workflows. Our multidisciplinary ML teams focus on a broad spectrum of areas, including Visual Generation Foundation Models, Multimodal Understanding, Visual Understanding of People, Text, Handwriting, and Scenes, Personalization, Knowledge Extraction, Conversation Analysis, Behavioral Modeling for Proactive Suggestions, and Privacy-Preserving Learning. These innovations form the foundation of the seamless, intelligent experiences our users enjoy every day. We are looking for senior research engineers to architect and advance multimodal LLM and Agentic AI technologies, ensuring their safe and responsible deployment in the real world. An ideal candidate will have the ability to lead diverse cross functional efforts spanning ML modeling, prototyping, validation and privacy-preserving learning. A strong foundation in machine learning and generative AI, along with a proven ability to translate research innovations into production-grade systems, is essential. Industry experience in Vision-Language multimodal modeling, Reinforcement and Preference Learning, Multimodal Safety, and Agentic AI Safety & Security would be important needs. SELECTED REFERENCES TO OUR TEAM’S WORK: - https://arxiv.org/pdf/2507.13575 - https://arxiv.org/pdf/2407.21075 - https://www.apple.com/newsroom/2024/12/apple-intelligence-now-features-image-playground-genmoji-and-more/ We are looking for a candidate with a proven track record in applied ML research. Responsibilities in the role will include training large scale-multimodal (2D/3D vision-language) models on distributed backends, deploying efficient neural architectures on device and private cloud compute, addressing emerging safety challenges to make the model/agents robust and aligned with human values. A key focus of the position is ensuring real-world quality, emphasizing model and agent safety, fairness, and robustness. You will collaborate closely with ML researchers, software engineers, and hardware and design teams across multiple disciplines. The core responsibilities include advancing the multimodal capabilities of large language models and strengthening AI safety and security for agentic workflows. On the user experience front, the work will involve aligning image and video content to the space of LLMs for visual actions and multi-turn interactions, enabling rich, intuitive experiences powered by agentic AI systems.
Locations
- Cupertino, California, United States 95014
Salary
Estimated Salary Rangemedium confidence
50,000,000 - 120,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- training large-scale generative modelsintermediate
- adapting large-scale generative modelsintermediate
- deploying large-scale generative modelsintermediate
- AI safetyintermediate
- multimodal understandingintermediate
- multimodal generationintermediate
- development of agentic systemsintermediate
- multimodal machine learningintermediate
- Visual Generation Foundation Modelsintermediate
- Multimodal Understandingintermediate
- Visual Understanding of Peopleintermediate
- Visual Understanding of Textintermediate
- Visual Understanding of Handwritingintermediate
- Visual Understanding of Scenesintermediate
- Personalizationintermediate
- Knowledge Extractionintermediate
- Conversation Analysisintermediate
- Behavioral Modelingintermediate
- Privacy-Preserving Learningintermediate
- architecting multimodal LLMintermediate
- advancing Agentic AI technologiesintermediate
- safe and responsible deploymentintermediate
- leading cross-functional effortsintermediate
- ML modelingintermediate
- prototypingintermediate
- validationintermediate
- machine learningintermediate
- generative AIintermediate
- translating research into production-grade systemsintermediate
- Vision-Language multimodal modelingintermediate
- Reinforcement Learningintermediate
- Preference Learningintermediate
- Multimodal Safetyintermediate
- Agentic AI Safetyintermediate
- Agentic AI Securityintermediate
- applied ML researchintermediate
- training large-scale multimodal modelsintermediate
- distributed backendsintermediate
- deploying efficient neural architecturesintermediate
- on-device deploymentintermediate
- private cloud computeintermediate
- addressing safety challengesintermediate
- model robustnessintermediate
- alignment with human valuesintermediate
- ensuring real-world qualityintermediate
- model safetyintermediate
- fairnessintermediate
- robustnessintermediate
- collaborating with ML researchersintermediate
- collaborating with software engineersintermediate
- collaborating with hardware teamsintermediate
- collaborating with design teamsintermediate
- advancing multimodal capabilities of large language modelsintermediate
- strengthening AI safety and securityintermediate
- agentic workflowsintermediate
- aligning image content to LLMsintermediate
- aligning video content to LLMsintermediate
- visual actionsintermediate
- multi-turn interactionsintermediate
Required Qualifications
- M.S. or PhD in Electrical Engineering/Computer Science or a related field (mathematics, physics or computer engineering), with a focus on computer vision and/or machine learning or comparable professional experience. (experience)
- Strong ML and Generative Modeling fundamentals (experience)
- Experience using one or more of the following: Pre-training or Post-training of Multimodal-LLMs, Reinforcement Learning, Distillation (experience)
- Familiarity with distributed training (experience)
- Proficiency in using ML toolkits, e.g., PyTorch (experience)
- You're aware of the challenges associated to the transition of a prototype into a final product (experience)
- Proven record of research innovation and demonstrated leadership in both applied research and development (experience)
Preferred Qualifications
- Experience with building & deploying AI agents, LLMs for tool use, and Multimodal-LLMs (experience)
Responsibilities
- We are looking for a candidate with a proven track record in applied ML research. Responsibilities in the role will include training large scale-multimodal (2D/3D vision-language) models on distributed backends, deploying efficient neural architectures on device and private cloud compute, addressing emerging safety challenges to make the model/agents robust and aligned with human values.
- A key focus of the position is ensuring real-world quality, emphasizing model and agent safety, fairness, and robustness. You will collaborate closely with ML researchers, software engineers, and hardware and design teams across multiple disciplines. The core responsibilities include advancing the multimodal capabilities of large language models and strengthening AI safety and security for agentic workflows. On the user experience front, the work will involve aligning image and video content to the space of LLMs for visual actions and multi-turn interactions, enabling rich, intuitive experiences powered by agentic AI systems.
Tags & Categories
Senior Generative AI Research Engineer, Multimodal, Agent Modeling - SIML
Apple
Senior Generative AI Research Engineer, Multimodal, Agent Modeling - SIML
Job Description
Are you passionate about Generative AI? Are you interested in working on groundbreaking generative modeling technologies to enrich billions of people? We are driving multiple initiatives focused on advancing generative models, and we are seeking technical leaders experienced in training, adapting and deploying large-scale generative models. This role emphasizes AI safety, multimodal understanding and generation, and the development of agentic systems that push the boundaries of what AI can achieve responsibly. We are the Intelligence System Experience (ISE) team within Apple’s software organization. The team operates at the intersection of multimodal machine learning and system experiences. It oversees a range of experiences such as System Experience (Springboard, Settings), Image Generation, Genmoji, Writing tools, Keyboards, Pencil & Paper, Generative Shortcuts - all powered by production scale ML workflows. Our multidisciplinary ML teams focus on a broad spectrum of areas, including Visual Generation Foundation Models, Multimodal Understanding, Visual Understanding of People, Text, Handwriting, and Scenes, Personalization, Knowledge Extraction, Conversation Analysis, Behavioral Modeling for Proactive Suggestions, and Privacy-Preserving Learning. These innovations form the foundation of the seamless, intelligent experiences our users enjoy every day. We are looking for senior research engineers to architect and advance multimodal LLM and Agentic AI technologies, ensuring their safe and responsible deployment in the real world. An ideal candidate will have the ability to lead diverse cross functional efforts spanning ML modeling, prototyping, validation and privacy-preserving learning. A strong foundation in machine learning and generative AI, along with a proven ability to translate research innovations into production-grade systems, is essential. Industry experience in Vision-Language multimodal modeling, Reinforcement and Preference Learning, Multimodal Safety, and Agentic AI Safety & Security would be important needs. SELECTED REFERENCES TO OUR TEAM’S WORK: - https://arxiv.org/pdf/2507.13575 - https://arxiv.org/pdf/2407.21075 - https://www.apple.com/newsroom/2024/12/apple-intelligence-now-features-image-playground-genmoji-and-more/ We are looking for a candidate with a proven track record in applied ML research. Responsibilities in the role will include training large scale-multimodal (2D/3D vision-language) models on distributed backends, deploying efficient neural architectures on device and private cloud compute, addressing emerging safety challenges to make the model/agents robust and aligned with human values. A key focus of the position is ensuring real-world quality, emphasizing model and agent safety, fairness, and robustness. You will collaborate closely with ML researchers, software engineers, and hardware and design teams across multiple disciplines. The core responsibilities include advancing the multimodal capabilities of large language models and strengthening AI safety and security for agentic workflows. On the user experience front, the work will involve aligning image and video content to the space of LLMs for visual actions and multi-turn interactions, enabling rich, intuitive experiences powered by agentic AI systems.
Locations
- Cupertino, California, United States 95014
Salary
Estimated Salary Rangemedium confidence
50,000,000 - 120,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- training large-scale generative modelsintermediate
- adapting large-scale generative modelsintermediate
- deploying large-scale generative modelsintermediate
- AI safetyintermediate
- multimodal understandingintermediate
- multimodal generationintermediate
- development of agentic systemsintermediate
- multimodal machine learningintermediate
- Visual Generation Foundation Modelsintermediate
- Multimodal Understandingintermediate
- Visual Understanding of Peopleintermediate
- Visual Understanding of Textintermediate
- Visual Understanding of Handwritingintermediate
- Visual Understanding of Scenesintermediate
- Personalizationintermediate
- Knowledge Extractionintermediate
- Conversation Analysisintermediate
- Behavioral Modelingintermediate
- Privacy-Preserving Learningintermediate
- architecting multimodal LLMintermediate
- advancing Agentic AI technologiesintermediate
- safe and responsible deploymentintermediate
- leading cross-functional effortsintermediate
- ML modelingintermediate
- prototypingintermediate
- validationintermediate
- machine learningintermediate
- generative AIintermediate
- translating research into production-grade systemsintermediate
- Vision-Language multimodal modelingintermediate
- Reinforcement Learningintermediate
- Preference Learningintermediate
- Multimodal Safetyintermediate
- Agentic AI Safetyintermediate
- Agentic AI Securityintermediate
- applied ML researchintermediate
- training large-scale multimodal modelsintermediate
- distributed backendsintermediate
- deploying efficient neural architecturesintermediate
- on-device deploymentintermediate
- private cloud computeintermediate
- addressing safety challengesintermediate
- model robustnessintermediate
- alignment with human valuesintermediate
- ensuring real-world qualityintermediate
- model safetyintermediate
- fairnessintermediate
- robustnessintermediate
- collaborating with ML researchersintermediate
- collaborating with software engineersintermediate
- collaborating with hardware teamsintermediate
- collaborating with design teamsintermediate
- advancing multimodal capabilities of large language modelsintermediate
- strengthening AI safety and securityintermediate
- agentic workflowsintermediate
- aligning image content to LLMsintermediate
- aligning video content to LLMsintermediate
- visual actionsintermediate
- multi-turn interactionsintermediate
Required Qualifications
- M.S. or PhD in Electrical Engineering/Computer Science or a related field (mathematics, physics or computer engineering), with a focus on computer vision and/or machine learning or comparable professional experience. (experience)
- Strong ML and Generative Modeling fundamentals (experience)
- Experience using one or more of the following: Pre-training or Post-training of Multimodal-LLMs, Reinforcement Learning, Distillation (experience)
- Familiarity with distributed training (experience)
- Proficiency in using ML toolkits, e.g., PyTorch (experience)
- You're aware of the challenges associated to the transition of a prototype into a final product (experience)
- Proven record of research innovation and demonstrated leadership in both applied research and development (experience)
Preferred Qualifications
- Experience with building & deploying AI agents, LLMs for tool use, and Multimodal-LLMs (experience)
Responsibilities
- We are looking for a candidate with a proven track record in applied ML research. Responsibilities in the role will include training large scale-multimodal (2D/3D vision-language) models on distributed backends, deploying efficient neural architectures on device and private cloud compute, addressing emerging safety challenges to make the model/agents robust and aligned with human values.
- A key focus of the position is ensuring real-world quality, emphasizing model and agent safety, fairness, and robustness. You will collaborate closely with ML researchers, software engineers, and hardware and design teams across multiple disciplines. The core responsibilities include advancing the multimodal capabilities of large language models and strengthening AI safety and security for agentic workflows. On the user experience front, the work will involve aligning image and video content to the space of LLMs for visual actions and multi-turn interactions, enabling rich, intuitive experiences powered by agentic AI systems.
Tags & Categories