Staff Research Engineer – Multimodal Generative Modelling

Synthesia

Staff Research Engineer at Synthesia creating multimodal generative models for AI video communication. Collaborating in R&D to enhance visual and interactive models across voice, text, and video.

Posted 7/17/2026full-timeRemote • 🇬🇧 United KingdomLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in generative modeling and large language models, with a focus on developing and optimizing multi-modal systems for interactive voice-video synthesis. Proficient in end-to-end training of deep learning models, ensuring high performance and natural interaction.

Highest-signal resume keywords

Generative ModelingLarge Language ModelsPyTorchDeep Learning Model TrainingTime-Series Modeling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Generative ModelingLarge Language ModelsDeep Learning Model TrainingTime-Series ModelingTokenizationModel OptimizationDistributed TrainingSoftware Engineering

Soft Skills

Problem SolvingCollaboration

Industry Keywords

Multi-Modal SystemsVoice-Video SynthesisEmotional ExpressivenessNatural Interaction

Tech Stack

Tools & technologies

PyTorch

About the role

Key responsibilities & impact

Shape our roadmap to create new model capabilities and unlock new functionality for our customer base, on both short and long time horizons.
Propose novel multi-modal system architectures (especially text and voice).
Develop and evaluate streaming and conversational systems for low-latency, interactive voice-video synthesis.
Design solutions that reinforce emotional expressiveness and natural interaction.
Implement and bring designs to life, from pretraining through post-training.
Ship models to production with optimised runtime to serve customers, and address their feedback thereafter.

Requirements

What you’ll need

Strong understanding of generative modelling, ideally applied to sequential or multimodal data.
Hands-on experience with large language models or similar transformer-based architectures.
High proficiency in PyTorch, including distributed training and model optimization.
A solid grasp of time-series modeling and tokenization, preferably in the context of audio, speech, or video.
Proven experience training deep learning models end-to-end, from data preparation through evaluation.
Strong general software engineering skills.

Benefits

Comp & perks

Health insurance
Flexible working arrangements
Professional development opportunities
Equipment allowances