Multimodal ML Engineer
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You willTrain and fine-tune large-scale multimodal models (vision-language, audio, speech, video) from scratch and from pretrained checkpoints.
Design experiments, build multimodal data pipelines, and train MoE architectures.
Build alignment pipelines (SFT, DPO, GRPO), optimize for production (quantization, distillation, streaming), and deploy end-to-end.
Define evaluation metrics that actually matter for the product.
Requirements3+ years training large-scale multimodal models.
Strong PyTorch and distributed training experience (DeepSpeed, FSDP).
Deep familiarity with multimodal architectures – LLaVA, Qwen-VL, InternVL, Audio Flamingo, Whisper, HuBERT, Conformer or similar.
Hands-on RLHF/alignment across modalities (GRPO, DPO, reward modeling).
Both audio and video experience required – sequence modeling for each, plus large-scale dataset curation and production inference optimization.
Relocation to Paris or London (hybrid) required.
BonusAudio signal processing fundamentals – spectrograms, mel features, noise reduction.
MoE architecture experience.
We offer
Competitive salary + equity.
Official employment, visa and relocation help.
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