AIML - Machine Learning Research Engineer, Generative AI
Apple
AIML - Machine Learning Research Engineer, Generative AI
Job Description
Join Apple’s Generative AI team in Zurich as a Machine Learning Engineer specializing in foundation model post-training! Our team advances reinforcement learning (RL) for agentic tool use, planning and reasoning to enhance Apple’s foundation models. Our work directly shapes Apple Intelligence features such as Siri—impacting billions of users—while contributing to state-of-the-art research. You’ll collaborate with a dedicated group of researchers in Zurich and work closely with Apple’s core Foundation Model teams in Cupertino and NY. In our team, you will: - Develop and scale RL methods to improve reasoning, instruction following, multi-turn dialogue, and reduce hallucinations in large language models. - Design and train agents with tool use, planning, and API integration to reliably accomplish tasks. - Build and refine reward models, evaluators, datasets, and simulation environments (e.g., for RLHF, RLAIF, and RLVF). - Run large-scale experiments, analyze results, and translate findings into both research contributions and practical improvements for Apple Intelligence. - Collaborate within a Europe-based team of ~35 RL/ML experts, coordinating closely with Apple’s foundation model groups in the U.S. We value researchers eager to explore the space between fundamental research and applied work—with opportunities to contribute to both scientific progress and real-world applications!
Locations
- Zurich, Zurich, Switzerland 8301
Salary
Estimated Salary Rangemedium confidence
25,000,000 - 50,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- reinforcement learning (RL)intermediate
- machine learning engineeringintermediate
- foundation model post-trainingintermediate
- agentic tool useintermediate
- planning and reasoningintermediate
- developing RL methodsintermediate
- scaling RL methodsintermediate
- improving reasoningintermediate
- instruction followingintermediate
- multi-turn dialogueintermediate
- reducing hallucinations in large language modelsintermediate
- designing agentsintermediate
- training agentsintermediate
- tool useintermediate
- API integrationintermediate
- building reward modelsintermediate
- refining reward modelsintermediate
- building evaluatorsintermediate
- refining evaluatorsintermediate
- building datasetsintermediate
- refining datasetsintermediate
- building simulation environmentsintermediate
- refining simulation environmentsintermediate
- RLHFintermediate
- RLAIFintermediate
- RLVFintermediate
- running large-scale experimentsintermediate
- analyzing resultsintermediate
- translating findings into research contributionsintermediate
- translating findings into practical improvementsintermediate
- collaborating with researchersintermediate
- coordinating with foundation model groupsintermediate
Required Qualifications
- MSc, PhD, or equivalent research/industry experience in Computer Science, Machine Learning, Electrical Engineering, or a related field. (experience)
- Strong background in reinforcement learning and deep learning, with hands-on experience training large-scale models, particularly LLMs. (experience)
- Proficiency in Python and modern ML frameworks (e.g., PyTorch, JAX), with demonstrated experience in distributed training. (experience)
- Ability to collaborate in interdisciplinary teams and clearly communicate complex concepts to both technical and non-technical partners. (experience)
Preferred Qualifications
- Publications in top ML/AI venues, or equivalent contributions through open-source or impactful industry work. (experience)
- Hands-on experience with tool use, planning, retrieval, and agentic integrations for LLMs. (experience)
- Experience with data curation, evaluation frameworks, and safety/guardrail methods. (experience)
- Ability to design and implement experiments at scale, and to develop innovative approaches to challenging problems. (experience)
Responsibilities
- In our team, you will:
- - Develop and scale RL methods to improve reasoning, instruction following, multi-turn dialogue, and reduce hallucinations in large language models.
- - Design and train agents with tool use, planning, and API integration to reliably accomplish tasks.
- - Build and refine reward models, evaluators, datasets, and simulation environments (e.g., for RLHF, RLAIF, and RLVF).
- - Run large-scale experiments, analyze results, and translate findings into both research contributions and practical improvements for Apple Intelligence.
- - Collaborate within a Europe-based team of ~35 RL/ML experts, coordinating closely with Apple’s foundation model groups in the U.S.
- We value researchers eager to explore the space between fundamental research and applied work—with opportunities to contribute to both scientific progress and real-world applications!
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Answer 10 quick questions to check your fit for AIML - Machine Learning Research Engineer, Generative AI @ Apple.
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AIML - Machine Learning Research Engineer, Generative AI
Apple
AIML - Machine Learning Research Engineer, Generative AI
Job Description
Join Apple’s Generative AI team in Zurich as a Machine Learning Engineer specializing in foundation model post-training! Our team advances reinforcement learning (RL) for agentic tool use, planning and reasoning to enhance Apple’s foundation models. Our work directly shapes Apple Intelligence features such as Siri—impacting billions of users—while contributing to state-of-the-art research. You’ll collaborate with a dedicated group of researchers in Zurich and work closely with Apple’s core Foundation Model teams in Cupertino and NY. In our team, you will: - Develop and scale RL methods to improve reasoning, instruction following, multi-turn dialogue, and reduce hallucinations in large language models. - Design and train agents with tool use, planning, and API integration to reliably accomplish tasks. - Build and refine reward models, evaluators, datasets, and simulation environments (e.g., for RLHF, RLAIF, and RLVF). - Run large-scale experiments, analyze results, and translate findings into both research contributions and practical improvements for Apple Intelligence. - Collaborate within a Europe-based team of ~35 RL/ML experts, coordinating closely with Apple’s foundation model groups in the U.S. We value researchers eager to explore the space between fundamental research and applied work—with opportunities to contribute to both scientific progress and real-world applications!
Locations
- Zurich, Zurich, Switzerland 8301
Salary
Estimated Salary Rangemedium confidence
25,000,000 - 50,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- reinforcement learning (RL)intermediate
- machine learning engineeringintermediate
- foundation model post-trainingintermediate
- agentic tool useintermediate
- planning and reasoningintermediate
- developing RL methodsintermediate
- scaling RL methodsintermediate
- improving reasoningintermediate
- instruction followingintermediate
- multi-turn dialogueintermediate
- reducing hallucinations in large language modelsintermediate
- designing agentsintermediate
- training agentsintermediate
- tool useintermediate
- API integrationintermediate
- building reward modelsintermediate
- refining reward modelsintermediate
- building evaluatorsintermediate
- refining evaluatorsintermediate
- building datasetsintermediate
- refining datasetsintermediate
- building simulation environmentsintermediate
- refining simulation environmentsintermediate
- RLHFintermediate
- RLAIFintermediate
- RLVFintermediate
- running large-scale experimentsintermediate
- analyzing resultsintermediate
- translating findings into research contributionsintermediate
- translating findings into practical improvementsintermediate
- collaborating with researchersintermediate
- coordinating with foundation model groupsintermediate
Required Qualifications
- MSc, PhD, or equivalent research/industry experience in Computer Science, Machine Learning, Electrical Engineering, or a related field. (experience)
- Strong background in reinforcement learning and deep learning, with hands-on experience training large-scale models, particularly LLMs. (experience)
- Proficiency in Python and modern ML frameworks (e.g., PyTorch, JAX), with demonstrated experience in distributed training. (experience)
- Ability to collaborate in interdisciplinary teams and clearly communicate complex concepts to both technical and non-technical partners. (experience)
Preferred Qualifications
- Publications in top ML/AI venues, or equivalent contributions through open-source or impactful industry work. (experience)
- Hands-on experience with tool use, planning, retrieval, and agentic integrations for LLMs. (experience)
- Experience with data curation, evaluation frameworks, and safety/guardrail methods. (experience)
- Ability to design and implement experiments at scale, and to develop innovative approaches to challenging problems. (experience)
Responsibilities
- In our team, you will:
- - Develop and scale RL methods to improve reasoning, instruction following, multi-turn dialogue, and reduce hallucinations in large language models.
- - Design and train agents with tool use, planning, and API integration to reliably accomplish tasks.
- - Build and refine reward models, evaluators, datasets, and simulation environments (e.g., for RLHF, RLAIF, and RLVF).
- - Run large-scale experiments, analyze results, and translate findings into both research contributions and practical improvements for Apple Intelligence.
- - Collaborate within a Europe-based team of ~35 RL/ML experts, coordinating closely with Apple’s foundation model groups in the U.S.
- We value researchers eager to explore the space between fundamental research and applied work—with opportunities to contribute to both scientific progress and real-world applications!
Tags & Categories
Answer 10 quick questions to check your fit for AIML - Machine Learning Research Engineer, Generative AI @ Apple.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs