Multimodal ML Engineer

Npv · Paris · Hybrid

• Employment typeFull-time
• Experience levelMid level
• Salary signalSalary not listed
• Last checked25 days ago
• Source valid throughOct 6, 2026

Role Overview

We're looking for a Multimodal ML Engineer to join White Circle , an AI Safety company building the policy enforcement and optimization layer for AI systems. Backed by $11M from senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, and DeepMind, White Circle processes 100M+ API calls monthly and runs its own LLMs in production. You will Train and fine-tune large-scale multimodal models (vision-language, audio, speech, video) from scratch and from pretrained checkpoints. Design experiments, build multimodal data pipelines, and train MoE architectures. Build alignment pipelines (SFT, DPO, GRPO), optimize for production (quantization, distillation, streaming), and deploy end-to-end. Define evaluation metrics that actually matter for the product. Requirements 3+ years training large-scale multimodal models. Strong PyTorch and distributed training experience (DeepSpeed, FSDP). Deep familiarity with multimodal architectures – LLaVA, Qwen-VL, InternVL, Audio Flamingo, Whisper, HuBERT, Conformer or similar. Hands-on RLHF/alignment across modalities (GRPO, DPO, reward modeling). Both audio and video experience required – sequence modeling for each, plus large-scale dataset curation and production inference optimization. Relocation to Paris or London (hybrid) required. Bonus Audio signal processing fundamentals – spectrograms, mel features, noise reduction. MoE architecture experience. We offer $100k–$250k/year salary + equity; higher figures can be negotiated. Official employment, visa and relocation help. Compensation: $100K – $250K • Higher figures and equity are negotiable • $100K – $250K • Higher figures and equity are negotiable Find more English Speaking Jobs in France on Arbeitnow

Why It Matches

This full-time role is listed as hybrid in Paris. The source does not expose a salary range, so confirm compensation before investing in a long application. The match score reflects role clarity, work mode, location, freshness, salary visibility, and description depth—not a promise that the employer will select a candidate.

Questions to Check Before Applying

• Does the source describe the same title, company, location, and work arrangement shown here?
• Which required skills can you prove with a result, project, metric, or certification?
• How often must you work on site, and is the commute practical for the stated schedule?
• When will the employer disclose the salary range and total compensation?

Career Guides for This Role