> Markdown version of [/jobs/ext/2726703-multimodal-ml-engineer](https://www.wearedevelopers.com/jobs/ext/2726703-multimodal-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Multimodal ML Engineer - **Company:** White Circle - **Location:** Paris, France - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Audio Signal Processing, Noise Reduction, Distributed Computing Environment, Software Safety, Pytorch, Large Language Models, Deep Learning, Machine Learning Operations, Software Version Control, Data Pipelines, Web Api, Data Generation - **Published:** September 5, 2026 - **Apply:** https://startup.jobs/multimodal-ml-engineer-white-circle-8682398 ## About the Role * 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 A big plus: * Understanding of audio signal processing fundamentals (spectrograms, mel features, noise reduction) ## Description * 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 ## Related Videos - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Web APIs you might not know about](https://www.wearedevelopers.com/videos/281-web-apis-you-might-not-know-about) - [Multimodal Generative AI Demystified](https://www.wearedevelopers.com/videos/829-multimodal-generative-ai-demystified) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Inside the Mind of an LLM](https://www.wearedevelopers.com/videos/1617-inside-the-mind-of-an-llm) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [The Best Large Language Models on The Market](https://www.wearedevelopers.com/magazine/319-the-best-large-language-models-on-the-market) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Dev Digest 138 - Are you secure about this?](https://www.wearedevelopers.com/magazine/486-dev-digest-138-are-you-secure-about-this) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)