Multimodal ML Engineer - Vision, Audio & Language (Hybrid)

United States Digital Space LLC; Paris; Hybride; EUR 86 000 - 215 000; Plein temps

Avantages offerts par ce poste

Equity

Visa and relocation assistance

Résumé du poste

White Circle, an AI Safety company, is hiring a Multimodal ML Engineer to build policy enforcement and optimization layers for AI systems. You will train and fine-tune large-scale multimodal models across vision-language and audio modalities, and deploy production-ready pipelines.

Relocation to Paris or London (hybrid) is required. The role requires hands-on RLHF, MoE architectures, and experience with PyTorch, DeepSpeed, and large-scale data curation.

Qualifications

• 3+ 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.

Responsabilités

• Train 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.

Connaissances

PyTorch

DeepSpeed

FSDP

RLHF

Multimodal models

MoE architectures

Data pipelines

Production inference

Outils

LLaVA

Qwen-VL

InternVL

Audio Flamingo

Whisper

HuBERT

Conformer

Description du poste

White Circle, an AI Safety company, is hiring a Multimodal ML Engineer to build policy enforcement and optimization layers for AI systems. You will train and fine-tune large-scale multimodal models across vision-language and audio modalities, and deploy production-ready pipelines.

Relocation to Paris or London (hybrid) is required. The role requires hands-on RLHF, MoE architectures, and experience with PyTorch, DeepSpeed, and large-scale data curation.