Multimodal ML Engineer

White Circle; Paris; Hybrid; EUR 90,000 - 130,000; Full time

Benefits offered by this job

Relocation package

Hybrid Paris work

Medical insurance (France-based)

Team off-sites

Job summary

White Circle is building an AI safety platform and seeks a Multimodal ML Engineer to train and ship vision, audio, video, and speech models for scalable safety policies. You will design experiments, build data pipelines, and optimize MoE architectures for production-ready inference.

You will work on end-to-end model deployment, evaluation benchmarks across visual QA, video understanding, and audio comprehension, with a hybrid Paris-based role and relocation support.

Qualifications

• 3+ years training large-scale deep learning models in multimodal domains.
• Strong PyTorch skills with hands-on distributed training experience (DeepSpeed, FSDP, or similar).
• Deep experience with multimodal architectures — encoders, projectors, and LLMs fit together (e.g., vision/audio encoders plus LLMs).
• Hands-on with RLHF/alignment for multimodal: GRPO, DPO, reward modeling.
• Experience with video and/or audio sequence modeling and streaming inference.

Responsibilities

• Train and fine-tune large-scale multimodal models 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 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 matter for the product: visual QA, spatial reasoning, video comprehension, speech and audio understanding.

Skills

PyTorch

Distributed training

Multimodal architectures

RLHF / alignment

Video/audio sequence modeling

MoE architectures

Data pipelines

Experiment design

Production optimization

Engineering fundamentals

Tools

DeepSpeed

FSDP

MoE

Job description

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 raised $11M from 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

• 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