Research Engineer, Multimodal
About the Role and Team Joining us as a Research Engineer on the Multimodal team, you'll be at the forefront of building and advancing video and image generation models that bring AI characters to life in entirely new ways. Your work will directly shape how millions of users experience rich, expressive, and visually co
What this role actually needs.
About the Role and Team Joining us as a Research Engineer on the Multimodal team, you'll be at the forefront of building and advancing video and image generation models that bring AI characters to life in entirely new ways. Your work will directly shape how millions of users experience rich, expressive, and visually co Responsibilities: - 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 across diverse visual scenarios. - Design and build large-scale data pipelines and automated annotation workflows to support continuous model improvement. - Explore model compression, inference acceleration, and serving optimizations to enable efficient real-time video processing at scale. Requirements: - Strong passion for pushing the boundaries of visual AI, with a self-driven, hands-on approach to solving complex technical problems - Proficient in PyTorch with end-to-end experience across data processing, model training, and deployment - Solid understanding of video and image generation architectures, including diffusion models, DiT, ControlNet, and SOTA video generation models - Experience with multimodal model training, including working with audio, vision, and language modalities together - Experience with distributed training tools (FSDP, DeepSpeed, etc.) - Experience with large-scale data processing, dataset construction, and automated data cleaning Company context: Character.AI builds personalized conversational AI agents and the infrastructure that powers them at scale.
Day-to-day expectations
Character.AI lists these responsibilities for the Research Engineer, Multimodal role.
- 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 across diverse visual scenarios.
- Design and build large-scale data pipelines and automated annotation workflows to support continuous model improvement.
- Explore model compression, inference acceleration, and serving optimizations to enable efficient real-time video processing at scale.
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- Strong passion for pushing the boundaries of visual AI, with a self-driven, hands-on approach to solving complex technical problems
- Proficient in PyTorch with end-to-end experience across data processing, model training, and deployment
- Solid understanding of video and image generation architectures, including diffusion models, DiT, ControlNet, and SOTA video generation models
- Experience with multimodal model training, including working with audio, vision, and language modalities together
- Experience with distributed training tools (FSDP, DeepSpeed, etc.)
- Experience with large-scale data processing, dataset construction, and automated data cleaning
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Source: jobs.ashbyhq.com · Source ID: d5a38f47-cd16-4e10-818d-45a4f845b529 · Confidence: 92/100 · Last checked: May 7, 2026
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