Senior Research Engineer — Generative Video & AI Systems

Dex; Greater London; Presencial; GBP 90.000 - 150.000; Jornada completa

Descripción de la vacante

Dex is seeking a research engineer to join a core modeling stream, focusing on end-to-end development of proprietary generative models for photorealistic synthetic media. You will influence conditioning, distributed training stability, and inference performance across large GPU clusters, taking ideas from hypothesis through production.

The role emphasizes hands-on engineering over academic work, with responsibility from research design to production deployment in a fast-moving startup

Formación

• 6+ years of hands-on ML or research engineering.
• Proven track record training deep learning models at scale.
• Experience with multi-node distributed training across GPUs.
• Experience in training diffusion architectures in video or audio.

Responsabilidades

• Own end-to-end research and engineering cycle for proprietary generative models, driving architectural hypotheses from small-scale validation to production deployment.
• Architect and execute distributed training recipes across multi-node, multi-GPU clusters under real compute constraints using FSDP, DeepSpeed, and sequence parallelism.
• Engineer conditioning mechanisms and training strategies that improve avatar control, temporal coherence, and motion fidelity without sacrificing photorealism.
• Isolate and resolve complex training instabilities, gradient anomalies, and throughput bottlenecks across large-scale model runs.
• Establish rigorous evaluation benchmarks and drive state-of-the-art inference optimizations to bridge the gap between offline model weights and low-latency product generation.

Conocimientos

Python

PyTorch

Distributed training

Diffusion models

Research engineering

Herramientas

FSDP

DeepSpeed

Sequence parallelism

DDP

Descripción del empleo

Dex is seeking a research engineer to join a core modeling stream, focusing on end-to-end development of proprietary generative models for photorealistic synthetic media. You will influence conditioning, distributed training stability, and inference performance across large GPU clusters, taking ideas from hypothesis through production.

The role emphasizes hands-on engineering over academic work, with responsibility from research design to production deployment in a fast-moving startup