Multimodal ML Engineer

Hybrid (Paris), Hybrid (London)Full-timePosted 3mo ago

About the role

Multimodal ML Engineer to train and ship vision, audio, video, and speech models for an AI safety platform that operates at 100M+ API calls/month.

Train and fine-tune large-scale multimodal models (vision-language, audio, speech) from scratch and from pretrained checkpoints
Extend models across modalities: image understanding, video temporal modeling, long-context processing, and streaming audio
Design and run experiments: architecture changes, data mixes, training recipes
Build and maintain multimodal data pipelines — from raw images, video, and audio recordings to training-ready datasets, including synthetic data generation
Train and optimize MoE architectures for efficient multimodal inference
Build alignment pipelines: SFT, DPO, GRPO, reward modeling — across modalities, not just text
Optimize models for production: quantization, distillation, batching, streaming and low-latency serving
Deploy models end-to-end: from research checkpoint to production serving
Define evaluation metrics and benchmarks that actually matter for the product: visual QA, spatial reasoning, video comprehension, speech and audio understanding

3+ years training large-scale deep learning models in multimodal domains (vision-language, audio, speech, or acoustic)
Strong PyTorch skills with hands-on distributed training experience (DeepSpeed, FSDP, or similar)
Deep experience with multimodal architectures — you understand how vision/audio encoders, projectors, and LLMs fit together (LLaVA, Qwen-VL, InternVL, Audio Flamingo, Omni Qwen, Audio Qwen, Whisper, HuBERT, Conformer, or similar)
Hands-on with RLHF/alignment for multimodal: GRPO, DPO, reward modeling — not just for text
Experience with video and/or audio sequence modeling: temporal modeling, long-context processing, efficient attention, streaming inference
Track record of shipping models to production: you've hit latency targets and optimized inference, not just reported benchmark scores
Comfortable with large-scale multimodal dataset curation: image-text pairs, video-instruction data, audio preprocessing, augmentation, synthetic data generation
Familiar with MoE architectures and their tradeoffs for multimodal workloads
Strong engineering fundamentals: clean code, version control, testing, documentation

Required:Multimodal model training

Competitive compensation package, including equity
Flexible time off
Paid time off in line with local regulations, no matter where you work from
Work from Paris or London (hybrid) + relocation package for Paris
Best medical insurance in France
Learning and development support for courses, conferences, and opportunities to grow your skills
All the hardware, tools, and services you need
Covered subscriptions for AI agents and IDEs
Team off-sites twice a year: Alps and Saint-Tropez

Last updated Sep 30, 2026. Interested? Apply on the company site.

We keep your AI models safe, reliable, and secure.

AI governanceAI safetycase studiescomplianceenterpriseheadline-ai-europe-100-2026model governancepolicy enforcement