Multimodal ML Engineer

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 https://whitecircle.ai/ 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