# Research Engineer, Multimodal

**Company:** [character.ai](https://hotfix.jobs/companies/characterai)
**Location:** Redwood City, CA
**Role:** ML Engineering
**Salary:** $225k – $400k/yr
**Skills:** PyTorch, Diffusion Models, Dit, Controlnet, Lora, RLHF, Fsdp, Deepspeed, Kubernetes, Docker
**Posted:** 2026-03-06

> Research Engineer advancing video/image generation models for AI characters, leading fine-tuning, novel architectures, data pipelines, and optimizations using PyTorch and multimodal techniques. Requires expertise in generation models and distributed training.

## Job Description

## What You'll Do
- Lead fine-tuning and continued training of video generation models, including image-to-video and joint audio-visual generation.
- Design and experiment with novel model architectures for multimodal generation, including multimodal conditioning (voice, structured text, reference images).
- Leverage techniques such as **LoRA**, **RLHF**, and full-parameter fine-tuning to improve model quality.
- Design and build large-scale data pipelines and automated annotation workflows.
- Explore model compression, inference acceleration, and serving optimizations for real-time video processing.

## Who You Are
- Strong passion for visual AI with hands-on problem-solving.
- Proficient in **PyTorch** with end-to-end experience in data processing, model training, and deployment.
- Solid understanding of video/image generation architectures (**diffusion models**, **DiT**, **ControlNet**, SOTA video models).
- Experience with multimodal model training (audio, vision, language).
- Experience with distributed training tools (**FSDP**, **DeepSpeed**).
- Experience with large-scale data processing and dataset construction.

## Nice to Have
- Experience with joint audio-visual or speech-conditioned generation.
- Experience with AIGC, video effects, character animation, or asset generation.
- Familiarity with ML deployment (**Kubernetes**, **Slurm**, **Docker**, cloud platforms).
- Publications in **NeurIPS**, **ICLR**, **CVPR**, **ECCV**, **ICCV**.

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