Multimodal ML Engineer

Moonfire; Greater London; Hybrid; GBP 120,000 - 170,000; Full time

Benefits offered by this job

Equity

Flexible time off

Paid time off

Relocation package

Job summary

White Circle is an AI Safety company building the safety, reliability, and optimization layer for AI systems. The team trains and deploys multimodal models across vision, audio, and video modalities to power safety policies at scale.

We seek an experienced Multimodal ML Engineer to lead end-to-end model development, from training from scratch to production serving, and to design robust evaluation benchmarks for real-world AI safety use cases.

Qualifications

• Proficient in PyTorch with experience in distributed training.
• Strong background in multimodal model architectures across vision/audio domains.
• Hands-on experience with RLHF/alignment for multimodal systems.
• Track record shipping models to production with latency targets.

Responsibilities

• Train and fine-tune large-scale multimodal models (vision-language, audio, speech).
• Extend models across modalities including video temporal modeling and streaming audio.
• Design experiments and data mixes; build training recipes.
• Build and maintain multimodal data pipelines and synthetic data generation.
• Train and optimize MoE architectures for efficient inference.
• Deploy models end-to-end from research checkpoint to production serving.
• Define metrics and benchmarks relevant to product needs.

Skills

PyTorch

Distributed training

Multimodal architectures

RLHF/alignment

Video/audio sequence modeling

Production deployment

Engineering fundamentals

Latency optimization

Tools

DeepSpeed

FSDP

Whisper

HuBERT

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 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

• 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

1. Introductory call with HR (25 min)
2. Take-home test assignment
3. Technical interview with Head of Applied Research (60 min)
4. Final conversation with CEO (45 min)