About this role
Summary: Join White Circle to train and fine-tune large-scale multimodal models, design alignment pipelines, and optimize production deployments. The role involves working with vision-language, audio, and video data to build safety and reliability layers for AI systems.
We're looking for a Multimodal ML Engineer to join *White Circle*, an AI Safety company building the safety, reliability, and optimization layer for AI systems through natural-language policies it automatically tests, enforces, and improves at scale. Backed by $70M (Series A) from top funds and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, and others, White Circle processes 100M+ API calls monthly and fine-tunes and trains its own LLMs to run faster and cheaper than open or proprietary models.
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
- Competitive salary + equity.
- Official employment, visa and relocation help.