# Multimodal ML Engineer at Whitecircle

- Company: Whitecircle (whitecircle.com)
- Location: Paris (remote)
- Places: Paris, Remote, France
- Posted: 2026-07-02
- Apply: https://jobs.ashbyhq.com/whitecircle/344ef31b-338b-4cbf-8d27-6f109611a44e
- Page: https://ye.saranatour.dev/jobs/multimodal-ml-engineer-whitecircle-kn712cph483jwdpjfgr489sa358hc7v0

## The posting

Multimodal ML Engineer
Whitecircle · Paris

**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](https://whitecircle.ai/) 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**

- Competit