Multimodal ML Engineer

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