Multimodal ML Engineer

Npv; Paris; Hybride; EUR 86 000 - 215 000; Plein temps

Avantages offerts par ce poste

Visa and relocation assistance

Equity

Résumé du poste

White Circle is seeking a Multimodal ML Engineer to join its AI Safety team, building the policy enforcement and optimization layer for AI systems. You will train and fine-tune large-scale multimodal models from scratch and from pretrained checkpoints, and design experiments and data pipelines to support robust development.

You will deploy end-to-end alignment pipelines (SFT, DPO, GRPO), optimize for production (quantization, distillation, streaming), and define evaluation metrics that truly

Qualifications

• 3+ years of experience training large-scale multimodal models.
• Strong PyTorch and distributed training experience (DeepSpeed, FSDP).
• Familiarity with multimodal architectures (LLaVA, Qwen-VL, InternVL, Audio Flamingo, Whisper, HuBERT, Conformer).
• Hands-on RLHF/alignment across modalities (GRPO, DPO, reward modeling).
• Experience with both audio and video sequence modeling and large-scale dataset curation for production inference.
• Relocation to Paris or London (hybrid) required.

Responsabilités

• Train and fine-tune large-scale multimodal models 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

Multimodal architectures

RLHF alignment

Audio/Video modeling

Production inference optimization

Outils

Distributed training tooling

GPU clusters

Description du poste

We're looking for a Multimodal ML Engineer to join White Circle, an AI Safety company building the policy enforcement and optimization layer for AI systems. Backed by $11M from senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, and DeepMind, White Circle processes 100M+ API calls monthly and runs its own LLMs in production.

You will

• 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.

Requirements

• 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.
• Relocation to Paris or London (hybrid) required.

Bonus

• Audio signal processing fundamentals – spectrograms, mel features, noise reduction.
• MoE architecture experience.

We offer

• $100k–$250k/year salary + equity; higher figures can be negotiated.
• Official employment, visa and relocation help.

Compensation: $100K – $250K Higher figures and equity are negotiable

• $100K – $250K Higher figures and equity are negotiable