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Anthropic London, ML Systems & Performance fellows—inference efficiency, quantization, on-device deployment directly match core strengths 1-2.

Anthropic London Senior SWE Inference—direct match: sub-100ms inference, quantization, KV caching, speculative decoding are explicit strengths.

Anthropic London Staff SWE Inference—staff level + inference efficiency core strength. Best-fit posting in the batch.

xAI London MTS Model Training—LLM post-training, fine-tuning, Llama-scale experience directly matches. Top-tier fit.

Anthropic London, AI Safety fellows program. Plausible research fit; fellowship format unclear for senior IC. Minimum 4 per policy.

Anthropic London, AI Security fellows. Less direct fit than ML/inference roles but Anthropic policy floor applies.

Anthropic London Research Engineer RL—post-training adjacent; RL for LLMs overlaps RLHF/fine-tuning experience. Strong Anthropic signal.

Anthropic London RL Velocity—training efficiency + RL overlaps post-training and inference work. Solid fit for senior IC.

Anthropic London Pretraining RE—less direct than post-training but Llama-scale training experience is relevant. Anthropic floor applies.

Anthropic London Pretraining Scaling—scaling laws/training overlap partially; post-training stronger suit but Anthropic floor applies.

Anthropic London Science of Scaling—research-heavy; publication record and MS CS relevant. Anthropic minimum floor applies.

Anthropic London Alignment Science—research fit moderate; patent/publications help. Anthropic policy floor, not core strength match.

Anthropic London Staff SWE AI Reliability—infra/platform adjacent; less direct than inference roles but staff level and Anthropic floor.

xAI London MTS Reasoning—LLM reasoning overlaps post-training/RLHF work; strong company, right level.

Helsing Foundation Models London—LLM fine-tuning, post-training, on-device deployment align well. Defence context is novel but role fits.

Helsing ML Engineering London—production ML, LLM deployment, efficiency work align strongly with candidate's applied stack.