Multimodal ML Engineer

white circle • United Kingdom

Relocation

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AI Summary

Multimodal ML Engineer to train and ship vision, audio, video, and speech models for an AI safety platform. Train and fine-tune large-scale multimodal models. Design and run experiments, build and maintain multimodal data pipelines.

Key Highlights

Train and fine-tune large-scale multimodal models

Design and run experiments, build and maintain multimodal data pipelines

Deploy models end-to-end: from research checkpoint to production serving

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Key Responsibilities

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

Technical Skills Required

PyTorch Distributed training Multimodal architectures

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Benefits & Perks

Paid time off in line with your local regulations

Comprehensive medical insurance

All the hardware, tools, and services you need

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Nice to Have

Understanding of audio signal processing fundamentals (spectrograms, mel features, noise reduction)

Job Description

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

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