Multimodal ML Engineer

White Circle

Multimodal ML Engineer developing and fine-tuning multimodal models for AI safety platform. Train and deploy vision, audio, and speech models at White Circle.

Posted 7/2/2026full-timeParis • 🇫🇷 FranceMid-LevelSenior💰 $120,000 - $250,000 per yearWebsite

Tech Stack

Tools & technologies

PyTorch

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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

Benefits

Comp & perks

Paid time off in line with your local regulations, no matter where you work from
Work from Paris (hybrid) with a relocation package available, or work from London (note: we are unable to provide relocation support for London-based roles)
Comprehensive medical insurance for our France-based team (please note that we are in the process of setting up our UK office and therefore cannot offer medical insurance for London-based roles yet)
All the hardware, tools, and services you need
Covered subscriptions for AI agents and IDEs
Team off-sites twice a year: we’ve recently been to the Alps and to Saint-Tropez

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Multimodal Model TrainingData Pipeline DevelopmentModel OptimizationQuantizationDistillationTemporal ModelingSynthetic Data GenerationMoE ArchitecturesEvaluation Metrics DefinitionClean Code Practices