PhD Intern, Machine Learning: MSI, Product Operations Team
Apple · Bengaluru · onsite · Posted 23d ago
Not disclosedFresherfulltimeSourced from
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About this role
Summary
Imagine what you could do here. At Apple, new ideas have a way of becoming phenomenal products, services, and customer experiences very quickly. Every single day, people do amazing things at Apple. This position involves a wide variety of skills, innovation, and is a rare opportunity to be working on groundbreaking, new applications of machine learning, research, and implementation. Ultimately, your work would have a huge impact on billions of users across the globe. You can help inspire change, by using your skills to influence globally recognized products manufacturing.
Description
The goal of Apple's Manufacturing and Operations team is to take a vision of a product and turn it into a reality. Through the use of statistics, the scientific process, and machine learning, the team recommends and implements solutions to the most challenging problems. We're looking for ML researchers to help us revolutionize how we manufacture Apple's amazing products. Put your skill & passion to work in this highly visible role.
Responsibilities
Learn about accessibility in Apple's workplace
Role Number: [phone removed]
Imagine what you could do here. At Apple, new ideas have a way of becoming phenomenal products, services, and customer experiences very quickly. Every single day, people do amazing things at Apple. This position involves a wide variety of skills, innovation, and is a rare opportunity to be working on groundbreaking, new applications of machine learning, research, and implementation. Ultimately, your work would have a huge impact on billions of users across the globe. You can help inspire change, by using your skills to influence globally recognized products manufacturing.
Description
The goal of Apple's Manufacturing and Operations team is to take a vision of a product and turn it into a reality. Through the use of statistics, the scientific process, and machine learning, the team recommends and implements solutions to the most challenging problems. We're looking for ML researchers to help us revolutionize how we manufacture Apple's amazing products. Put your skill & passion to work in this highly visible role.
Responsibilities
- Research, build & adapt ML approaches for domain adaptation of multimodal foundation models to Apple manufacturing. Domain adaptation here is broad — spanning modalities including text, images, and video, such as generating physics-consistent videos adapted to Apple manufacturing
- Develop techniques that steer large foundation models without modifying their weights, and multimodal knowledge-graph reasoning — domain-specific Q&A, multi-hop reasoning, disambiguation, and tool/retrieval orchestration over manufacturing knowledge
- Train, evaluate, and analyze model performance (e.g., exact-match/F1 accuracy, explanation quality, and generation fidelity) on public benchmarks and on internal datasets where applicable
- Apply and extend LLMs, VLMs, and World Models to advance domain adaptation for manufacturing
- Collaborate within Apple to apply solutions to industrial problems
- Conduct presentations of analyses and results to a wide range of audiences including executives
- Pursuing PhD in Computer Science / related field
- Deep understanding of Machine Learning concepts, Probability and Statistics
- Understanding of Computer Vision & VLMs, with demonstrated skill in training models and applying them to ML application development.
- Ability to prototype research ideas as working code (Python)
- Ability to meaningfully present results of analysis in a clear and impactful manner
- Familiarity with applying Machine Learning, especially Computer Vision in a Manufacturing / Assembly setting
- Familiarity with multimodal understanding & reasoning, knowledge-graphs & generative video models
- Prior publications in relevant field
Learn about accessibility in Apple's workplace
Role Number: [phone removed]
Sourced from foundit
Skills
multimodal knowledge-graph reasoningprobabilityComputer VisionstatisticsLarge Language Models (LLMs)World ModelsVLMsPythonMachine Learning
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