White Circle
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 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) 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)
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 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) 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)
White Circle
TLDR: We are looking for an ML Infrastructure Engineer to build the systems behind our LLM post training, RL, evaluation, inference, and agentic development workflows. You will work close to researchers, GPUs, training loops, data control systems, evals, inference stacks, and the infrastructure decisions that directly affect model learning and product quality. 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 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 Build robust, flexible, and scalable RL and post training pipelines, including smoke tuning runs for quality testing and approach ablations Design data control systems that govern what the model sees, when it sees it, and how training data flows through rollouts, replay, filtering, evaluation, and policy updates Tune training and inference end to end for high throughput across the systems that matter: networking, memory, compute scheduling, data loading, storage, checkpointing, and I/O Investigate how infrastructure choices affect learning dynamics, eval quality, model behavior, and training stability - staying close to the state of the art in LLMs, RL, and post training Build infrastructure for model iteration: experiment runs, artifacts, evals, dashboards, failure inspection, reproducibility, and cost visibility Work on inference infrastructure where it affects post training and evaluation loops Build and improve agentic development environments: coding agent harnesses, browser/tool integrations, terminal/runtime sandboxes, repo aware workflows, and multi agent orchestration Work closely with the team: plan future steps, discuss tradeoffs, share context early, and stay in touch while building You'll fit right in if you Have designed, built, or maintained distributed RL/post training systems at scale and are fluent in their moving parts: rollouts, replay buffers, reward signals, data filtering, policy updates, evaluation loops, and failure analysis Are familiar with deep learning frameworks such as PyTorch or JAX Are proficient in Python, including concurrency, asynchronous programming, multiprocessing, and performance optimization Can debug distributed GPU workloads across CUDA runtime, container runtime, driver versions, NCCL or equivalent communication layers, networking, storage, scheduling, and checkpointing Have experience with profiling tools across the stack, for example py spy, PyTorch profiler, Nsight, perf, tracing, metrics, logs, or custom instrumentation Have experience with inference stacks such as vLLM, SGLang, TensorRT LLM, Dynamo, or custom serving infrastructure Can reason from system metrics back to model behavior: when latency, queueing, sampling, data order, rollout throughput, or infrastructure failures affect learning Have a strong ownership mindset: you can take an ambiguous infrastructure problem, make it concrete, ship a working system, and improve it from real feedback A big plus A public builder footprint: open source contributions to RL, distributed ML, LLM training, inference, eval, or agent infrastructure - repos, PRs, benchmarks, papers with code, technical posts - and a good technical X/Twitter presence with live building, debugging threads, and useful interaction with strong builders Experience in a high bar AI infra, research, or model environment such as xAI/Grok, Qwen, ByteDance AI infra/research, Prime Intellect, or similar teams Custom training framework support or ownership: distributed training, fine tuning pipelines, trainers, schedulers, checkpointing, data loaders, model/eval integration, or performance tooling Serious use of Claude Code, Codex, Kimi Code, Pi Agent, Droid, or similar agentic coding systems as a development surface Experience with GPU clusters on Kubernetes, Slurm, Ray, custom schedulers, or cloud GPU orchestration NCCL, UCX, NVSHMEM, RDMA, InfiniBand, RoCE, or EFA Rust, C++, CUDA, Go, or systems level performance work Why White Circle You will be able to propose and run your own experiments and research ideas on modern ML infrastructure with very little friction You will work on current ML infra problems, close to research, product needs, and real model iteration - not maintaining legacy systems for the sake of keeping them alive You will have an unusually high contribution level for the size of the team; your systems decisions can change how quickly we train, evaluate, ship, and improve models You will have room to dig into areas of your own interest, as long as they help the company build better, faster, safer AI systems 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 currently unable to provide relocation support and medical insurance for London based roles). Comprehensive medical insurance for our France based team. 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.
TLDR: We are looking for an ML Infrastructure Engineer to build the systems behind our LLM post training, RL, evaluation, inference, and agentic development workflows. You will work close to researchers, GPUs, training loops, data control systems, evals, inference stacks, and the infrastructure decisions that directly affect model learning and product quality. 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 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 Build robust, flexible, and scalable RL and post training pipelines, including smoke tuning runs for quality testing and approach ablations Design data control systems that govern what the model sees, when it sees it, and how training data flows through rollouts, replay, filtering, evaluation, and policy updates Tune training and inference end to end for high throughput across the systems that matter: networking, memory, compute scheduling, data loading, storage, checkpointing, and I/O Investigate how infrastructure choices affect learning dynamics, eval quality, model behavior, and training stability - staying close to the state of the art in LLMs, RL, and post training Build infrastructure for model iteration: experiment runs, artifacts, evals, dashboards, failure inspection, reproducibility, and cost visibility Work on inference infrastructure where it affects post training and evaluation loops Build and improve agentic development environments: coding agent harnesses, browser/tool integrations, terminal/runtime sandboxes, repo aware workflows, and multi agent orchestration Work closely with the team: plan future steps, discuss tradeoffs, share context early, and stay in touch while building You'll fit right in if you Have designed, built, or maintained distributed RL/post training systems at scale and are fluent in their moving parts: rollouts, replay buffers, reward signals, data filtering, policy updates, evaluation loops, and failure analysis Are familiar with deep learning frameworks such as PyTorch or JAX Are proficient in Python, including concurrency, asynchronous programming, multiprocessing, and performance optimization Can debug distributed GPU workloads across CUDA runtime, container runtime, driver versions, NCCL or equivalent communication layers, networking, storage, scheduling, and checkpointing Have experience with profiling tools across the stack, for example py spy, PyTorch profiler, Nsight, perf, tracing, metrics, logs, or custom instrumentation Have experience with inference stacks such as vLLM, SGLang, TensorRT LLM, Dynamo, or custom serving infrastructure Can reason from system metrics back to model behavior: when latency, queueing, sampling, data order, rollout throughput, or infrastructure failures affect learning Have a strong ownership mindset: you can take an ambiguous infrastructure problem, make it concrete, ship a working system, and improve it from real feedback A big plus A public builder footprint: open source contributions to RL, distributed ML, LLM training, inference, eval, or agent infrastructure - repos, PRs, benchmarks, papers with code, technical posts - and a good technical X/Twitter presence with live building, debugging threads, and useful interaction with strong builders Experience in a high bar AI infra, research, or model environment such as xAI/Grok, Qwen, ByteDance AI infra/research, Prime Intellect, or similar teams Custom training framework support or ownership: distributed training, fine tuning pipelines, trainers, schedulers, checkpointing, data loaders, model/eval integration, or performance tooling Serious use of Claude Code, Codex, Kimi Code, Pi Agent, Droid, or similar agentic coding systems as a development surface Experience with GPU clusters on Kubernetes, Slurm, Ray, custom schedulers, or cloud GPU orchestration NCCL, UCX, NVSHMEM, RDMA, InfiniBand, RoCE, or EFA Rust, C++, CUDA, Go, or systems level performance work Why White Circle You will be able to propose and run your own experiments and research ideas on modern ML infrastructure with very little friction You will work on current ML infra problems, close to research, product needs, and real model iteration - not maintaining legacy systems for the sake of keeping them alive You will have an unusually high contribution level for the size of the team; your systems decisions can change how quickly we train, evaluate, ship, and improve models You will have room to dig into areas of your own interest, as long as they help the company build better, faster, safer AI systems 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 currently unable to provide relocation support and medical insurance for London based roles). Comprehensive medical insurance for our France based team. 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.