Midjourney India Data Scientist Interview: Rounds, Questions and Preparation

What the job involves

Data scientists in AI and deeptech work on generative models, recommendation systems, and data pipelines that power creative tools. At a fast-growing company like Midjourney India, you would clean and curate large image-text datasets, fine-tune diffusion models, and run experiments to improve generation quality and speed. The role blends coding, statistics, and product intuition to ship model improvements quickly.

Role and technical knowledge

1. 1

   Explain how a diffusion model generates an image from noise.

   Cover the forward noising process, reverse denoising with a U-Net, and how the model learns to predict noise at each timestep.

2. 2

   What metrics would you use to evaluate the quality of generated images, and why?

   Mention FID for distribution similarity, CLIP score for text-image alignment, and human evaluation; note their limitations.

3. 3

   How would you handle a dataset of 10 million image-text pairs with noisy captions?

   Discuss filtering by CLIP similarity, deduplication, caption cleaning with heuristics or a language model, and sampling strategies.

4. 4

   What is the role of latent space in models like Stable Diffusion?

   Explain how a VAE compresses images to a lower-dimensional latent space, reducing compute while preserving perceptual quality.

Practical scenarios

1. 5

   Your model generates beautiful images but often ignores the text prompt. How do you debug and fix this?

   Check classifier-free guidance scale, text encoder quality, and training data alignment; suggest fine-tuning with hard negatives or reweighting loss.

2. 6

   You need to reduce inference time by 50 percent without a big drop in quality. What steps do you take?

   Propose distillation, fewer sampling steps with a better scheduler, quantization, or using a smaller latent model; mention measuring trade-offs.

3. 7

   Design an A/B test to compare two image generation models for a creative tool.

   Define success metrics like user retention, generation count, or prompt adherence; randomize users, control for prompt difficulty, and run for statistical power.

4. 8

   A user reports that the model produces biased or unsafe images. How would you investigate and mitigate?

   Audit training data for stereotypes, add safety filters, use negative prompts during training, and set up a feedback loop for red-teaming.

Behavioural and HR

1. 9

   Why do you want to work at a fast-growing AI company like Midjourney India rather than a large tech firm?

   Show enthusiasm for generative AI, ownership, and rapid iteration; connect your skills to the company's mission of creative tools.

2. 10

   Tell me about a time you had to learn a new ML technique quickly for a project.

   Use STAR format: describe the project, the gap, how you learned (paper, code, mentor), and the outcome with a metric.

3. 11

   Describe a situation where your model failed or gave poor results. How did you handle it?

   Emphasize debugging steps, seeking feedback, and iterating; show resilience and a learning mindset rather than blaming data.

4. 12

   How do you prioritize tasks when you have multiple model experiments running and a deadline?

   Mention impact vs effort, communicating with stakeholders, automating repetitive parts, and focusing on the highest-value experiment first.

Coding and problem solving

1. 13

   Write a Python function to compute the cosine similarity between two batches of image and text embeddings.

   Use NumPy or PyTorch, normalize vectors, and handle batch dimensions; discuss efficiency for large batches.

2. 14

   Given a list of prompts and their generated images, how would you find the top 5 worst generations by CLIP score?

   Compute CLIP scores, sort, and return indices; mention vectorization and avoiding loops for speed.

3. 15

   Implement a simple data loader that yields batches of image-text pairs with shuffling.

   Use PyTorch Dataset and DataLoader, handle padding for text, and ensure reproducibility with a seed.

4. 16

   How would you detect and remove duplicate images from a large dataset?

   Use perceptual hashing or embedding similarity with a threshold; discuss trade-offs between exact and near-duplicate detection.