New opportunity

Machine Learning Researcher, Image/ Video Generation

Apple · Singapore

About this role

Apple is hiring a researcher in Singapore to develop image and video generation and editing capabilities, including multimodal foundation models for visual understanding and generation. The role combines applied machine learning research with engineering for AI-powered product features.

Responsibilities

Develop, fine-tune and evaluate foundational video generation and editing models and unified multimodal foundation models.
Develop domain-specific video generation and editing models for tasks and applications in Apple products.
Conduct generative AI research and transfer research advances into production-ready technologies.
Translate product requirements into modeling and engineering tasks, collaborating with researchers and engineers across Apple. The team’s broader work includes image, video and 3D generation, editing and animation.

Minimum qualifications:

A bachelor’s degree in Electrical Engineering, Computer Science or Computer Engineering, with 3 years of relevant work experience.
Experience in machine learning, deep learning and statistical modeling; computer vision models for tasks such as object detection and visual question answering; and image generation models such as VAEs, GANs and diffusion models.
Proficiency in one of PyTorch, JAX or TensorFlow, and one of Python, Go, Java or C++.

Preferred qualifications

An MS or PhD in Electrical Engineering, Computer Science or Computer Engineering; fresh MS/PhD graduates are welcome to apply.
Experience developing state-of-the-art image and video generation or editing models, along with strong interpersonal and teamwork skills.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Machine LearningDeep LearningStatistical ModelingComputer VisionComputer GraphicsImage GenerationVideo GenerationVideo EditingMultimodal Foundation Models3D GenerationObject DetectionVisual Question AnsweringVariational AutoencodersGenerative Adversarial NetworksDiffusion ModelsModel Fine-TuningModel EvaluationPyTorchJAXTensorFlowPythonGoJavaC++

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Foundational video and multimodal models
The role develops, fine-tunes and evaluates foundational video generation and editing models and unified models for visual understanding and generation. Prepare to explain how you would compare modeling approaches and evaluate their capabilities.
Product-specific video capabilities
The role also develops, fine-tunes and evaluates domain-specific video generation and editing models for tasks in Apple’s AI-powered products. Prepare to discuss how a defined use case would shape model adaptation and evaluation.
Research-to-production transfer
The posting calls for generative AI research that becomes production-ready technology, supported by solid engineering skills. Prepare an example of how you would test a research advance beyond a promising initial result and make it usable in a product setting.
Translate requirements across disciplines
The researcher must turn product requirements into modeling and engineering tasks and collaborate with engineers and researchers across Apple. Prepare to explain how you would clarify a requirement and divide it into testable technical work.

Likely challenges to think through

Balancing model breadth with targeted performance
Likely preparation challenge, not a stated employer problem: a unified model for understanding and generation may need different choices from a domain-specific video model. Be ready to explain when to adapt a broad model and when a narrower approach would better serve a defined task.
Evaluating generated and edited video
Likely preparation challenge, not a confirmed employer problem: assessing video output may require balancing visual quality, temporal consistency and whether an edit follows its intended instruction. Prepare a concrete evaluation plan that distinguishes these concerns rather than relying on a single score.
Turning research gains into product capabilities
Likely preparation challenge, not a stated employer problem: an improved research result may introduce engineering complexity or fail to meet a product requirement. Prepare to discuss how you would verify the gain, identify the trade-off and work with collaborators on the next modeling or engineering step.

Practice your answers

Technical practice

How would you compare VAEs, GANs and diffusion models for image generation?

What to coverSuggested preparation: Explain how each model is trained and where its strengths and limitations matter for image quality, diversity and controllability. Make clear which comparisons are general modeling trade-offs rather than requirements stated for this role.

How would you use object detection and visual question answering to assess visual understanding?

What to coverSuggested preparation: Describe what each task measures, how you would evaluate performance, and where a model might succeed on one task but fail on the other. Connect the answer to the posting’s interest in models capable of both understanding and generation.

What makes video generation harder to evaluate than image generation?

What to coverSuggested preparation: Discuss temporal consistency alongside per-frame quality, and explain how you would inspect failure cases over time. These are suggested evaluation considerations; the posting does not prescribe particular metrics.

How would you approach fine-tuning a model for video editing?

What to coverSuggested preparation: Define what the edit should change and what it should preserve, then discuss training data, adaptation choices and evaluation of the result. Consider the trade-off between following an edit instruction and maintaining coherent content across frames.

How could a unified multimodal model support both visual understanding and generation?

What to coverSuggested preparation: Explain how representations and training objectives might serve both capabilities, and identify a possible conflict between them. Relate your answer to the stated responsibility for unified multimodal foundation models without assuming a specific architecture.

How would you adapt a foundational video model to a domain-specific product task?

What to coverSuggested preparation: Start with the task definition and relevant data, then explain what you would fine-tune and how you would compare the adapted model with the foundation model. Discuss the risk of improving the target task while weakening broader capabilities.

How would you evaluate a video generation or editing model before deciding what to improve?

What to coverSuggested preparation: Propose task-specific tests, qualitative review and an analysis of recurring failure modes. Distinguish evidence of model improvement from a result that looks better on only a narrow set of examples.

How would you translate a product requirement into a modeling and engineering plan?

What to coverSuggested preparation: Show how you would turn a desired visual capability into a defined task, an evaluation approach and implementation steps. Identify assumptions you would clarify with collaborators rather than inventing product requirements.

How would you decide whether a generative-AI research advance is ready for production engineering?

What to coverSuggested preparation: Explain how you would reproduce the result, test it against the intended task and identify limitations that engineering would need to address. Frame quality, reliability and resource-use trade-offs as suggested considerations, not stated Apple acceptance criteria.

How would you troubleshoot a model fine-tuning run in a framework you know?

What to coverSuggested preparation: Use one of the listed frameworks—PyTorch, JAX or TensorFlow—to describe checks for data, loss behavior, gradients and evaluation discrepancies. Explain how you would isolate the cause before changing the model.

Behavioural practice

Tell me about a time you collaborated with people from different technical backgrounds on a research problem.

What to coverSuggested preparation: Use situation, action and result to show how you aligned terminology, divided work and incorporated others’ expertise. Choose an example that makes your own contribution clear.

Tell me about a time you turned an unclear product goal into a tractable technical task.

What to coverSuggested preparation: Describe the situation, the questions you asked, the modeling or engineering decisions you made, and the result. Be clear about how you checked that the task still reflected the original goal.

Tell me about a time a model experiment failed or gave a misleadingly good result.

What to coverSuggested preparation: Explain the situation, how you diagnosed the evidence, what you changed and the result. Focus on your reasoning and how the finding affected the next experiment.

Tell me about a time you helped move a research result toward an engineering application.

What to coverSuggested preparation: Describe the situation, your actions to communicate limitations or make the work usable, and the result. If the work did not reach production, state its actual outcome rather than implying that it did.

Sharpen your knowledge

Topics to prepare

Image generation model families
The minimum qualifications name VAEs, GANs and diffusion models. Suggested revision exercise: compare their training objectives, sampling approaches and common failure modes, then explain when each might suit an image-editing task.
Video generation and editing
Developing, fine-tuning and evaluating foundational and domain-specific video models are core responsibilities. Suggested revision exercise: sketch how a still-image generation approach would need to change for temporal consistency and controlled video edits.
Unified multimodal models
The role calls for foundation models capable of both visual understanding and generation. Suggested revision exercise: diagram how a model could handle a visual question and a generation request, and identify what each task would need to evaluate.
Computer vision and graphics foundations
The posting seeks computer vision/graphics experience and specifically names object detection and visual question answering. Suggested revision exercise: explain the inputs, outputs and likely error cases for both named vision tasks, then relate them to generation or editing.
Machine learning and statistical modeling
Machine learning, deep learning and statistical modeling are minimum-qualification experience areas. Suggested revision exercise: outline how you would separate training and evaluation data and diagnose whether an apparent model improvement generalizes.

Skills to revise

Fine-tune and evaluate a video model
The responsibilities explicitly include fine-tuning and evaluating video generation and editing models. Suggested hands-on exercise: run a small-scale fine-tuning comparison, inspect generated outputs and document how you would check edit fidelity and temporal consistency; these checks are preparation suggestions, not stated employer criteria.
Implement in a listed framework and language
The posting requires proficiency in one of PyTorch, JAX or TensorFlow and one of Python, Go, Java or C++. Suggested hands-on exercise: use a framework and language you know from those lists to implement a compact training-and-evaluation loop, then explain its debugging choices.
Build a vision-task baseline
Experience developing models for tasks such as object detection and visual question answering is explicitly requested. Suggested hands-on exercise: implement or adapt a small baseline for one of those tasks, inspect incorrect predictions and explain what the errors reveal.
Translate a product need into a modeling plan
The role includes turning product requirements into modeling and engineering tasks and transferring research to production-ready technology. Suggested hands-on exercise: take a hypothetical video-editing request and write a brief plan covering model behavior, data needs, evaluation and engineering handoff; the scenario is practice, not a stated Apple project.

Your preparation checklist

Prepare a video-model walkthrough
The role involves developing, fine-tuning and evaluating video generation and editing models. If you have relevant work, prepare one walkthrough covering the task, model choices, experiments, evaluation and limitations.
Rehearse a multimodal design exercise
The role calls for unified models capable of visual understanding and generation. Sketch how you would frame such a task, choose training and evaluation approaches, and test trade-offs; present this as a proposed approach, not an Apple project.
Organize evidence for the stated technical qualifications
Map available examples to machine learning, statistical modeling, object detection or visual question answering, and VAE, GAN or diffusion models. Be ready to explain your contribution and results without implying experience you do not have.
Select code examples to discuss
The posting asks for proficiency in one of PyTorch, JAX or TensorFlow and one of Python, Go, Java or C++. Choose examples you can explain, including implementation decisions, debugging and reproducibility.
Practice translating a product need into research tasks
The role includes turning product requirements into modeling and engineering tasks and transferring research toward production-ready technology. Prepare a hypothetical video-editing brief with a task definition, evaluation plan and engineering considerations.
Prepare collaboration examples and employer questions
The posting emphasizes work across researchers and engineers. Prepare an example of a technical collaboration, then ask how this team prioritizes foundational versus domain-specific models and evaluates progress toward product use.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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