TL;DR: Multimodal ML Engineer to train and ship vision, audio, video, and speech models for an AI safety platform handling 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 modelling — not just for text

Experience with video and/or audio sequence modelling: temporal modelling, 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 trade-offs 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

Introductory call with HR (25 min)

Take-home test assignment

Technical interview with Head of Applied Research (60 min)

Final conversation with CEO (45 min)