Multimodal ML Engineer

White Circle; Paris; Hybride; EUR 122 613 - 201 436; Plein temps

Avantages offerts par ce poste

Relocation package

Hybrid work from Paris

Medical insurance (France)

All the hardware, tools & services

Subscriptions for AI agents & IDEs

Team off-sites

Résumé du poste

White Circle is hiring a Multimodal ML Engineer in Paris. You will train and ship vision, audio, video, and speech models for an AI safety platform processing 100M+ API calls monthly.

You will extend models across modalities, design experiments, and build multimodal data pipelines for training-ready datasets, including synthetic data generation. You will deploy models end-to-end, define evaluation benchmarks, and optimize for production latency.

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 and how encoders/projectors/LLMs fit together.
• Hands-on with RLHF/alignment for multimodal beyond text.
• Experience with video/audio sequence modeling and long-context processing.
• Track record of shipping models to production with latency optimization.
• Comfort with large-scale multimodal dataset curation and synthetic data generation.
• Familiar with MoE architectures and their tradeoffs for multimodal workloads.
• Strong engineering fundamentals: clean code, version control, testing, documentation

Responsabilités

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

Connaissances

Deep learning

PyTorch

Distributed training

Multimodal architectures

RLHF/alignment

Video/audio sequence modeling

MoE architectures

Model deployment

Data curation

Engineering fundamentals

Outils

DeepSpeed

FSDP

PyTorch tooling

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 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

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) is a plus

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

How We Hire

• Introductory call with HR (25 min)
• Take‑home test task
• Technical interview with Head of Applied Research (60 min)
• Final conversation with our CEO (45 min)

Compensation Range: $120K - $250K