Multimodal ML Engineer

White Circle; Paris; Hybride; EUR 90 000 - 140 000; Plein temps

Avantages offerts par ce poste

Hybrid work model

Relocation package for Paris

Best medical insurance in France

Learning and development support

Résumé du poste

White Circle is hiring a Multimodal ML Engineer to train and ship vision, audio, video, and speech models for an AI safety platform handling 100M+ API calls monthly. You will build end-to-end multimodal systems and optimize for production readiness.

The role requires 3+ years of experience with multimodal DL, PyTorch, and distributed training. This position offers hybrid work from Paris or London, with relocation support for Paris and strong medical insurance.

Qualifications

• 3+ years of experience training large-scale multimodal DL models.
• Strong PyTorch skills with distributed training experience (DeepSpeed, FSDP, or similar).
• Experience with multimodal architectures integrating vision, audio, and language models.
• Hands-on RLHF/alignment for multimodal: GRPO, DPO, reward modeling.
• Experience with video and audio sequence modeling and streaming inference.
• Track record of shipping models to production with latency targets.

Responsabilités

• Train and fine-tune large-scale multimodal models (vision-language, audio, speech) from scratch and pretrained checkpoints.
• Extend models across modalities: image understanding, video temporal modeling, long-context processing, streaming audio.
• Design and run experiments: architecture changes, data mixes, training recipes.
• Build and maintain multimodal data pipelines from raw images, video, and audio to training-ready datasets.
• Train and optimize MoE architectures for efficient multimodal inference.
• Build alignment pipelines: SFT, DPO, GRPO, reward modeling across modalities.
• Optimize models for production: quantization, distillation, batching, streaming, low-latency serving.
• Deploy models end-to-end from research checkpoint to production serving.
• Define evaluation metrics and benchmarks relevant to the product.

Connaissances

Multimodal DL

PyTorch

Distributed training

MoE architectures

RLHF alignment

Video/audio sequence modeling

Production deployment

Data curation

Engineering fundamentals

Outils

DeepSpeed

FSDP

LLaVA

Qwen-VL

Whisper

Audio Qwen

Conformer

Description du poste

TLDR:

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.

About us

White Circle is an AI Safety company building the safety, reliability, and optimization layer for AI systems. At the core of our platform are policies - simple natural-language rules that define what an AI model should and shouldn't do. We automatically test, enforce, and continuously improve these policies at scale.

• We've recently raised our Series A funding round, taking our total funding to $70M. Our investors include top funds, founders, and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, Datadog, Sentry, and others
• We process over 100M+ API calls every month
• We fine-tune and train our own LLMs so they run faster and cheaper than any open or proprietary model

We're a small, highly focused team. If you want to work deeply on hard problems, see your work ship to production quickly, and influence how AI safety is actually built - you're the one we need.

You will

• 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

You’ll fit right in if you

• 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)

Why White Circle

• Competitive compensation package, including equity
• Flexible time off
• Paid time off in line with your 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: we've recently been to the Alps and to Saint-Tropez

Process

1. Introductory call with HR (25 min)
2. Take-home test assignment
3. Technical interview with Head of Applied Research (60 min)
4. Final conversation with CEO (45 min)