General

General Questions

1. Tell me why you will be a good fit for the position. Why do you want to work for X?

I believe I’m a strong fit for this position because of my experience in [specific area relevant to the role, e.g., building scalable web applications] and my ability to quickly adapt to new technologies and challenges. I’ve successfully led projects where I collaborated with cross-functional teams, meeting tight deadlines while maintaining high-quality standards. Additionally, I’m genuinely excited about X company’s work in [specific area, e.g., innovative AI solutions], and I’m eager to bring my technical expertise and passion for problem-solving to the team.

I’m impressed by X company’s innovative work in [specific area, e.g., cloud solutions or AI development]. I admire your commitment to [a value or mission, e.g., open-source contributions or sustainability]. As someone who [your relevant strength, e.g., enjoys building scalable systems], I’m excited about the opportunity to contribute to your team and learn from such talented engineers.

2. Why do you want to leave your current/last company?

My last role @Meta provided me with great experience in increasing my scope beyond a Individual Contributor as I worked with Directors and VPs across multiple departments across the country on unifying multiple AR and AI product roadmaps into a cohesive strategy. I enjoyed the increased organizational scope, but I’m looking for a better balance like i did @Samsung, where I traveled a bit less and had more hands-on design and coding end-to-end over supporting several prototypes at the end of the pipeline. X company’s focus on [specific projects or technologies] makes this role a great next step in my career.

3. How to Explain Being Laid Off or a Contract Ending Early

Unfortunately, I was part of a company-wide re-org that ended my contract short. Since I worked directly with VPs and Directors and the re-org targeted them in particular, my position was impacted. It was a tough situation, but it gave me the chance learn modern day AR/AI pipelines and push agendas I care about. I’m excited to bring those skills to a new team where I can continue to make an impact

4. What are you looking for in your next role?

I’m looking for a role where I can work on challenging pipelines and collaborate with a multidisciplinary team to launch an impactful product. [Relate interest to role]

5. What frustrates you?

I find it frustrating when there’s a lack of clarity and communications in project requirements because it can lead to inefficiencies. Since i work at the intersection of Art and Engineering,  It is my job to identify this. However, I’ve learned to address this by asking clarifying questions early, documenting expectations, and ensuring alignment with stakeholders. One issue we had in our preprocessing pipeline is that the delivery requirements was not communicated with each team at the time, because the Artists want to deliver the highest quality data which took additional processing time which lead to 2 weeks. I discovered by talking to both sides that what requirements were needed and optimized the piepeline (talk about 2 weeks to 1 day pipeline) It’s rewarding to turn that initial uncertainty into a well-defined plan that everyone can follow.

6. Give an example of a time you received critical feedback. How did you respond?

Early in my career, my manager pointed out that I have trouble retaining information in the mornings, but not the afternoons, which lead to me asking redundant questions in the morning at times. They encouraged me to take the same focus i exhibit in the afternoon to mornings. On a more personal note, i didnt know at the time i was suffering from Sleep Apnea, so every morning i woke up with headaches. I solved this buy carrying a notebook around to jot everything down. A physical notebook, as intitally i used my cellphone to take notes but people felt like i wasnt listening when taking notes on cellphone.  I then put it in the notes in the secure internal wiki. I also took my health more seriously and went to a few doctors appt which lead to my diagnosis. Now, not only do i take notes at work, it just became a habit to take notes in life. I maintain my own personal wiki that i maintain for skil building like cooking and working out, and i also use Flashcards at night to help me maintain my knowledge via a question answer format. 

7. Where Do You See Yourself in Five Years?

In five years, I see myself growing both technically and professionally in a role that challenges me and allows me to make a meaningful impact and launching a successful product. Specifically, I aim to deepen my expertise in [specific area, e.g., distributed systems, machine learning, or front-end optimization] and take on more leadership responsibilities again like i did at Samsung, whether that’s mentoring junior engineers or leading projects. I’m excited about the opportunity to contribute to X company’s goals and grow with the team as we tackle innovative challenges together.

8. Describe a situation where you had to explain a complex idea to a non-technical person.

Describe something more machine learning centric?

The internet is like a giant postal system, where data (like a letter) travels between computers (addresses) using servers (post offices) to guide it.

Cloud computing is like renting storage and tools in a warehouse instead of owning them. Instead of buying expensive hardware, you can use someone else’s equipment and only pay for what you need, like storing photos or running applications. It’s convenient because you can access it from anywhere with the internet.

9. Tell me about a time you had a disagreement with your manager.

Situation: "When out product was exiting R&D and going into Production, manager and I disagreed on how much resources we should put into our Digital Asset Management system. "
Task: "Having worked at Sony when they were hacked, I care a lot about keeping data secure." 
Action: "I was given a budget to implement our DAM system. I decided to go for an Open Source solution and hiring a technically minded Digital Asset Manager "
Result: "We launched the DAM and product went into Production.   

10. Tell me about a time when you had a conflict with a co-worker. 

Pipeline Example:

Situation: In one project, a teammate and I disagreed on the best approach for implementing a feature. He preferred a quick fix, while I believed a scalable solution was better long-term."
Task: "We needed to agree on an implementation to meet the deadline."
Action: "I initiated a conversation to understand his concerns and shared my perspective with data showing the benefits of scalability. We collaborated to find a middle ground by implementing a solution that was scalable but prioritized immediate needs."
Result: "This not only resolved the conflict but also improved our collaboration and led to a successful project delivery."

Product Example:

Situation: "During a sprint, I proposed refactoring part of the codebase to improve maintainability, but a senior developer opposed it, citing time constraints."
Task: "I needed to convince the team that the refactor was critical without jeopardizing timelines."
Action: "I gathered data showing the technical debt risks and prepared a proposal to divide the refactor into smaller tasks over multiple sprints. I also ensured the changes wouldn’t delay immediate deliverables."
Result: "The team agreed with the plan, and we successfully reduced technical debt while staying on track with deadlines.

11. How Do You Use AI to Increase Productivity in Your Work?

  1. Highlight specific tools or techniques: Mention the AI tools you use (e.g., GitHub Copilot, ChatGPT, TensorFlow) and how they assist you.
  2. Show impact: Explain how AI improves efficiency, accuracy, or creativity in your tasks.
  3. Demonstrate adaptability: Reflect your ability to integrate emerging AI technologies into your workflow.
  4. Mention to not send anything proprietary to AI outside of the company

I use AI in several ways to increase productivity in my work. For instance, I use GitHub Copilot to streamline coding by suggesting boilerplate code or offering solutions for repetitive tasks. This allows me to focus more on solving complex problems and refining the architecture of my applications. I also leverage tools like ChatGPT for brainstorming solutions, generating technical documentation, or debugging code when I encounter roadblocks.

In addition, I use AI-powered analytics tools to identify patterns in application performance metrics, helping me optimize features and reduce latency. Incorporating AI into my workflow has not only sped up my output but also enhanced the quality of my deliverables by reducing errors and freeing up time for creative problem-solving."

Explaining a technical topic to someone without a technical background requires breaking down complex concepts into simple, relatable terms. Here's how you can do it effectively

12. Have you ever worked on a cross-functional team? What role did you play, and how did you ensure collaboration?

Talk about leading a small team at Samsung composted of engineer and designers 

Talk about Meta experience workign with Directors and VP 

Software Engineering Questions

13. What project are you currently working on?

Technical Artist (Gaussian Splats)

I'm currently working on a project that involves optimizing an API for a high-traffic e-commerce platform. My role includes improving response times and implementing caching strategies to reduce server load. It's been exciting to see how small changes in code and architecture can significantly enhance user experience and system performance.

Software Engineer (Scavenge AR) 

I'm currently working on a project that involves optimizing an API for a high-traffic e-commerce platform. My role includes improving response times and implementing caching strategies to reduce server load. It's been exciting to see how small changes in code and architecture can significantly enhance user experience and system performance.

14. Tell me about a time you solved a difficult technical problem

Technical Artist

The most challenging aspect of my current project is ensuring high availability while transitioning to a new cloud provider. We need to maintain uptime during the migration, which requires careful planning and thorough testing of failover strategies. I've been collaborating closely with the team to simulate different failure scenarios and refine our approach.

Software Engineer  

The most challenging aspect of my current project is ensuring high availability while transitioning to a new cloud provider. We need to maintain uptime during the migration, which requires careful planning and thorough testing of failover strategies. I've been collaborating closely with the team to simulate different failure scenarios and refine our approach.

15. What was the most difficult bug that you fix?

Technical Artist

I recently fixed a memory leak in a microservice that caused intermittent crashes during peak traffic. Identifying the leak was challenging because it only occurred under specific load conditions. Using tools like Valgrind and custom logging, I traced the issue to a third-party library that wasn’t releasing resources properly. I updated the library and wrote additional tests to ensure it didn’t recur. It was a great reminder of the importance of monitoring and profiling in production systems.

Software Engineer 

I recently fixed a memory leak in a microservice that caused intermittent crashes during peak traffic. Identifying the leak was challenging because it only occurred under specific load conditions. Using tools like Valgrind and custom logging, I traced the issue to a third-party library that wasn’t releasing resources properly. I updated the library and wrote additional tests to ensure it didn’t recur. It was a great reminder of the importance of monitoring and profiling in production systems.

16. Have you ever had to advocate for using a particular technology or framework? How did you influence your team?

Centralized storage for digital assets like images, videos, audio, and datasets.
Metadata tagging, categorization, and version control for easy search and retrieval.

ML models require large, well-organized datasets for training and inference. ResourceSpace ensures assets are:
Organized: Proper tagging and metadata allow for quick filtering by specific attributes (e.g., image resolution, format, or labels).
Easily Accessible: Centralized data prevents duplication and streamlines data access.

Metadata can serve as labels or features for supervised learning models.
Example: Images tagged with “dog” or “cat” can be directly used for classification tasks.
Streamlines the labeling process, reducing the time required for manual data preparation.
3. Version Control and Asset History

Tracks versions of assets, ensuring changes are logged and reversible.
Allows you to compare different versions of assets.
Why It’s Useful for ML Pipelines:

Training datasets evolve over time, and having version control ensures:
Consistency: ML models can be retrained on the same dataset versions.
Traceability: You can roll back to previous versions if a new dataset causes unexpected model behavior.
4. Integration with ML Pipelines
What ResourceSpace Provides:Bulk export tools for transferring large datasets to ML pipeline systems.

Programmatic access via APIs allows:
Automation: Automate data extraction and preprocessing for your pipeline.
Scalability: Easily handle large datasets and integrate with cloud-based pipelines (e.g., AWS SageMaker, TensorFlow, or PyTorch).
Bulk exports simplify transferring datasets to training environments.
5. Security and Permissions
Encryption and secure file transfers.
Why It’s Useful for ML Pipelines:

Ensures data security and compliance, especially when handling sensitive datasets (e.g., medical images, financial records).
Role-based permissions allow only authorized personnel or systems to access and modify datasets, reducing errors and ensuring auditability.
6. Streamlined Preprocessing

Support for custom workflows and batch operations (e.g., resizing images, converting file formats).
Plugins for extended functionality.

Preprocessing (e.g., resizing images or normalizing data) is often required before feeding data into ML models. ResourceSpace can handle:
Batch preprocessing: Prepares assets for direct use in ML workflows.
Data normalization: Ensures assets meet the pipeline’s input requirements.
7. Collaboration and Audit Trails

Collaboration features for teams to manage and annotate datasets.
Detailed logs of who accessed or modified assets.
Why It’s Useful for ML Pipelines:

Efficient Dataset Management: Multiple team members can contribute to cleaning, labeling, or organizing the dataset.
Accountability: Audit trails help track changes and identify potential data issues that may impact model performance.
Here’s how ResourceSpace DAM can integrate into an ML production pipeline:

Data Ingestion:

Upload raw assets (e.g., images, videos) into ResourceSpace.
Use metadata fields to tag assets with relevant information (e.g., labels, source, resolution).
Data Selection:

Query the ResourceSpace database for specific subsets of data (e.g., “images tagged as ‘cat’ with resolution > 1080p”).
Use API calls to retrieve assets programmatically.
Preprocessing:

Perform bulk operations like resizing, cropping, or format conversion within ResourceSpace.
Export preprocessed data to the ML pipeline environment.
Pipeline Integration:

Use ResourceSpace APIs to feed data directly into ML pipelines.
Automate periodic updates to the dataset by syncing ResourceSpace with cloud storage or ML frameworks.
Model Training and Evaluation:

Use the exported dataset to train ML models.
Feedback Loop:

In one project, I had to push for a Digital Asset Management system for our Final Preprocessing before data is passed off to Machine Learning. Our old way was publishing our data to a file system, but it lead to alot of issue where the data was still being touched by other teams. This lead to shortcuts where researchers at time would modify the data after QC, which lead to occasional bad ML results. Bad results were fine for R&D but not acceptable for a product and the data preprocessing team was held responsible at times. By implementing a DAM, the data was pulled through an API, was more secure because training data cannot be accessed via filesystem, and also had an interface where they could view diagnostic information and search through data via metadata. For instance, like looking at all our idle poses at once. This made the data safer and more exploratory. The best thing about this DAM? We used an opensource platform that cost us negligble about of money to implement. 

17. Tell me about a project where you faced unexpected challenges. How did you handle them?

Focus: Adaptability, resilience, and creativity.

Building Motion capture lab asap

  1. Problem, we thought 2d data was enough but realized we need 3d data. 3d data from mediapipe is poor
  2. Start with rented equipment. I used my connections at Magic Leap to find the best price for data. Solves short term problem
  3. Getting Vendor option from Real Mocap 
  4. Narrow down machine learning requirements
    1. Art team didnt ask question about delivery other than deliver best quality data
      1. includes so many render passes we dont need
    2. Research group take data and transcode them to smaller data for ML 
  5. Experiment with AI, off the shelf and repos 
  6. Build System and make sure the limits of the 

Situation
Task
Action
Result

18. Tell me about a time you met a tight deadline. Tell me about a time you had to prioritize tasks in a large project. How did you decide what to focus on?

Deadline for Leapcon and Royalshakeaspeare. 

Our team was tasked with delivering a critical feature for a client demo in just two weeks."
Task: "I needed to ensure the feature was fully functional and aligned with the client’s requirements within the deadline."
Action: "I worked with the team to define the MVP, prioritized key tasks, and streamlined communication to avoid delays. We worked extra hours when necessary and conducted daily stand-ups to track progress."
Result: "We delivered the feature on time, and the demo was a success. It reinforced the importance of prioritization and maintaining focus under pressure.

19. Describe a time when you had to refactor legacy code. How did you approach it?

20. Describe a project where you improved the performance of a system.

scavengeAR?

21. Describe a project where you improved the scalability of a system.

Renderfarm 

22. Can you give an example of a time you made a mistake in your code? How did you fix it?

ScavengeAR. made everythign in HLSL.

Creating an entire Unity UI in HLSL (High-Level Shader Language) instead of using Unity's Canvas system can be problematic due to several technical and practical reasons. While HLSL is powerful for creating custom visual effects, using it exclusively for a UI introduces significant challenges that make it less suitable compared to Unity's Canvas-based system. Here's why:

1. Complexity of UI Layout and Interaction
Canvas:

Unity's Canvas system provides built-in tools for layout management, such as anchors, pivots, and RectTransforms.
Easily handles dynamic resizing, positioning, and responsiveness across various screen sizes and resolutions.
Includes event systems for detecting clicks, drags, and other user interactions (e.g., buttons, sliders).
HLSL:

HLSL is primarily designed for rendering and lacks the concept of layout or user interaction.
To recreate layout management in HLSL, you would need to manually calculate positions, handle transformations, and account for screen resolution changes, which is extremely time-consuming.
Implementing interactive elements like buttons or sliders would require additional logic in scripts, effectively recreating Unity’s existing UI framework from scratch.
2. Lack of Accessibility Features
Canvas:

Unity's UI system supports accessibility features such as screen readers and keyboard navigation.
You can easily add animations, transitions, and tooltips to UI elements.
HLSL:

You would need to manually program accessibility features, which is not only challenging but also prone to errors.
Building animations and transitions would require custom shader logic, making maintenance and iteration harder.
3. Performance Considerations
Canvas:

Unity's Canvas system is optimized for UI rendering. The engine batches and manages draw calls efficiently for most common UI use cases.
Unity provides tools like Canvas Scalers to adjust the UI for different screen sizes without extra performance overhead.
HLSL:

Writing the entire UI in HLSL would require a full-screen quad (or multiple quads) to render elements, which means every pixel might be processed unnecessarily.
Without careful optimization, this approach can result in excessive GPU usage, especially if shaders include complex calculations for every frame.
4. Lack of Unity Editor Integration
Canvas:

The Canvas system integrates seamlessly with the Unity Editor, allowing you to design UI visually with tools like the RectTransform Editor and Prefabs.
Designers and artists can contribute without needing to write code or shaders.
HLSL:

Designing a UI in HLSL would require writing code for every single visual element and interaction.
This lack of a visual editor makes the workflow slower and limits collaboration with non-programmers.
5. Debugging and Maintenance
Canvas:

The Canvas-based UI leverages Unity's debugging tools, including the Scene view and UI event system.
Issues like misaligned elements or broken interactions are easy to identify and fix.
HLSL:

Debugging shader-based UI involves interpreting pixel-level behavior, which is far less intuitive.
Small changes to the design could require significant rework of shader code.
6. Scalability
Canvas:

Unity's UI system scales well for typical 2D and 3D applications, supporting features like nested canvases, localization, and animations.
It’s easy to add or remove UI elements without disrupting the entire layout.
HLSL:

Adding new UI elements in HLSL requires modifying shader code, which can make the system fragile and error-prone.
Scaling the UI to different screen sizes or adding responsive layouts becomes a major challenge.
When to Use HLSL for UI
HLSL can still be a good choice for specific visual effects in the UI, such as:

Creating custom shaders for buttons, text, or backgrounds (e.g., animated gradients, outlines, or glows).
Implementing unique effects like holographic or glitch effects for menus.
Enhancing Canvas-based UI with shaders rather than replacing it entirely.
In these cases, HLSL complements the Unity Canvas rather than replacing it, allowing you to benefit from the strengths of both.

Conclusion
Using HLSL to create the entire Unity UI is not recommended because:

It lacks the layout, interaction, and accessibility features of Unity's Canvas system.
It introduces unnecessary complexity and performance overhead.
Maintenance and iteration become significantly harder.
Instead, leverage Unity's Canvas system for the core UI structure and use HLSL sparingly to add custom visual effects. This approach balances usability, performance, and flexibility, ensuring a more robust and maintainable solution.

Technical Art Questions

23. Tell me about a time you optimized a 3D asset pipeline.

24. Have you ever worked on a project where the artistic vision conflicted with technical constraints? How did you balance them?

Performance is critical. Art is about hitting the essence, not hitting exactlt the concept art. 

Focus: Negotiation, technical expertise, and artistic understanding.

25. Tell me about a time you implemented a tool or workflow that improved efficiency for your team.

Start with experience building multiple tools (like pyqt tools, photogrammetry, etc), but talk avbout the QC preprocess pipeline 

Focus: Tool development and process improvements.

build Preprocessing for samsung

26. Give an example of a time you had to troubleshoot a rendering or asset issue in production.

Focus: Debugging and technical understanding. Learning from multiple occurances. 

3D? Deadline logs, understand trends in graphs 

2D? QCtools and preview diagnostics

Eval Sheet

codinginterviewevalcriteria.jpg

Information Overload

In "The Organized Mind," Daniel J. Levitin explores how the modern world’s overwhelming amount of information impacts our ability to think clearly and make decisions. Drawing on insights from psychology, neuroscience, and cognitive science, Levitin provides practical strategies for organizing our thoughts, lives, and environments to improve productivity and mental clarity. Here are ten key lessons and insights from the book:
1. The Information Age Challenge: Levitin discusses the challenges posed by the Information Age, where we are bombarded with an excess of information. This overload can lead to cognitive overload, making it difficult to focus and make effective decisions.
2. The Role of Attention: The author emphasizes the importance of attention in organizing our thoughts and actions. He explains that our brains have limited attentional resources, and learning to manage and direct our attention is crucial for productivity and clarity.
3. Cognitive Offloading: Levitin introduces the concept of cognitive offloading, which refers to the practice of using external tools (like lists, calendars, and apps) to manage information and tasks. By offloading cognitive tasks, we can free up mental resources for more complex thinking.
4. The Importance of Structure: The book highlights the significance of creating structure in our lives. Levitin suggests organizing our environments, schedules, and tasks in ways that reduce chaos and enhance our ability to focus on what matters.
5. Categorization and Chunking: Levitin explains how our brains process information more effectively when it is categorized or "chunked." By grouping similar items or tasks together, we can enhance memory retention and streamline our decision-making processes.
6. Mindfulness and Presence: The author discusses the benefits of mindfulness and being present in the moment. Practicing mindfulness can help reduce distractions, improve focus, and enhance our ability to engage with the task at hand.
7. Creating Routines: Levitin advocates for the development of routines as a way to minimize decision fatigue. Establishing regular habits and rituals can reduce the number of decisions we need to make, allowing us to conserve mental energy for more important tasks.
8. The Power of Sleep: The book underscores the critical role of sleep in cognitive functioning. Levitin explains how adequate rest is essential for memory consolidation, emotional regulation, and overall mental clarity, and he encourages prioritizing sleep in our lives.
9. Emotional Regulation: Levitin emphasizes the connection between organization and emotional regulation. A well-organized life can lead to reduced stress and anxiety, while chaos and disorganization can exacerbate emotional challenges.
10. The Social Brain: Finally, the author highlights the significance of social connections. Maintaining relationships and social networks is essential for mental well-being and plays an important role in how we organize our lives and manage stress.
In "The Organized Mind," Daniel J. Levitin provides a comprehensive framework for understanding how to navigate the complexities of modern life. By applying these ten key lessons and insights, readers can develop practical strategies for organizing their thoughts, tasks, and environments, ultimately leading to enhanced productivity and improved mental clarity. The book serves as a valuable resource for anyone seeking to thrive in an increasingly information-rich world.