Preparing for a Research Scientist role at Genmo requires a balanced approach. You must demonstrate that you are both an imaginative researcher capable of developing novel algorithms and a highly capable engineer who can write production-grade training code.

Generative AI Deep Domain Expertise – You must have an intuitive and mathematical grasp of diffusion models, autoregressive models, or flow matching. Be prepared to go to the whiteboard to derive loss functions, write down attention formulations, or explain the mechanics of latent space projections.

Systems & Engineering Execution – At Genmo, researchers write code. You will be evaluated on your proficiency with PyTorch, distributed training frameworks, and memory optimization techniques. You should be comfortable discussing the practical realities of scaling models to billions of parameters.

Problem-Solving & Ambiguity – Video generation is an active research frontier with many unsolved problems. Interviewers will present you with open-ended challenges—such as improving motion dynamics or reducing spatial distortion—to observe how you structure experiments, isolate variables, and iterate toward a solution.

Collaborative Research & Communication – You will need to present your past research clearly to a diverse group of scientists and engineers. You must demonstrate the ability to take feedback, collaborate across functional teams, and align your research directions with the company's broader product goals.

The interview process at Genmo is structured to evaluate your technical depth, research creativity, and cultural alignment with a fast-paced, high-impact research lab. The process moves quickly, and you will interact directly with core members of the research and founding teams.

The loop begins with a technical screen focused on your background and core machine learning fundamentals. This is followed by a deep dive into your research history, where you will present your work to the team. The onsite stage consists of rigorous technical sessions covering generative theory, video architecture design, coding, and systems scaling, culminating in conversations about product alignment and culture.

The timeline above represents the standard progression for the Research Scientist loop. While the entire process is typically completed within three to four weeks, the scheduling can be accelerated for exceptional candidates with competing offers. Each stage is designed to be highly interactive, giving you a clear window into the daily technical challenges the team solves.

To succeed in the Genmo interview loop, you must demonstrate mastery across several distinct technical domains. Below is a detailed breakdown of what our interviewers look for in each key evaluation area.

Diffusion & Generative Architectures

This area evaluates your theoretical understanding of how generative models learn complex data distributions, with a particular focus on diffusion processes and transformer-based backbones.

Be ready to go over:

• Noise Schedules and Samplers – The impact of linear, cosine, and sigmoid schedules on video quality; the mechanics of high-order ODE solvers (e.g., DPMSolver) for fast sampling.
• Latent Spaces – How spatial-temporal autoencoders compress high-dimensional video into low-dimensional latent space while preserving fine details and motion.
• Conditioning Mechanisms – How text prompts, image prompts, or layout controls are injected into the model architecture (e.g., via cross-attention or adaptive layer normalization).
• Advanced concepts (less common) – Discrete diffusion, consistency models, and multi-modal joint latent spaces.

Example scenarios:

• "Explain how you would modify a standard latent diffusion model to accept an initial image and a text prompt to perform highly realistic image-to-video generation."
• "If your video model is generating highly detailed static frames but struggles with coherent motion, how would you diagnose and fix the issue?"

Temporal Modeling & Video Consistency

Video is not just a sequence of independent images; it requires a deep understanding of physical dynamics, object permanence, and temporal flow. This area tests your ability to model the time dimension effectively.

Be ready to go over:

• Temporal Attention – Implementing causal versus non-causal attention across the frame dimension and managing the quadratic complexity of long-context attention.
• Motion Representation – How models learn optical flow, velocity, and physical interactions without explicit supervision.
• Evaluation Metrics – Programmatic measurement of temporal consistency, video quality, and text alignment using FVD, CLIP-T, and human-in-the-loop frameworks.

Example scenarios:

• "Design an architecture that can generate a continuous 10-second video at 30fps without running out of GPU memory during inference."
• "How would you handle the training of a video model on a dataset containing a mix of rapid action sequences and highly static landscape shots?"

Post-Training, Alignment & RLHF

This area is critical for candidates interviewing for the Post-training track. It focuses on taking a highly capable base model and refining it to produce safe, aesthetically pleasing, and instruction-adherent videos.

Be ready to go over:

• Preference Optimization – Adapting alignment algorithms like DPO, IPO, or KTO to the continuous, high-dimensional output space of diffusion models.
• Reward Model Architecture – Designing and training a reward model that can evaluate complex video attributes like motion fluidness, aesthetic quality, and prompt adherence.
• SFT Data Curation – Strategies for generating, filtering, and balancing high-quality caption-video pairs for instruction tuning.

Example scenarios:

• "Describe how you would build a pipeline to align a video diffusion model to avoid generating physical implosions or unrealistic object morphing."
• "How do you balance the trade-off between strict prompt adherence (alignment) and visual creativity (diversity) during post-training?"

Large-Scale Distributed Training & Optimization

At Genmo, training models requires massive computational resources. This area evaluates your ability to write efficient, scalable, and robust training code.

Be ready to go over:

• Distributed Frameworks – Deep understanding of PyTorch Distributed, FSDP, DeepSpeed, and Megatron-LM.
• Memory Management – Techniques like activation checkpointing, CPU offloading, and FP8 training to maximize batch size per GPU.
• Scaling Diagnostics – Identifying and resolving training instabilities, gradient explosions, and hardware bottlenecks in large-scale cluster environments.

Example scenarios:

• "You are launching a 512-GPU training run and notice that loss spikes consistently after 10k steps. Walk us through your diagnostic process."
• "Write pseudo-code for a custom PyTorch training loop that implements gradient accumulation and mixed-precision training safely."