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Anthropic Fellows ML Systems & Performance – directly matches inference efficiency, quantization, on-device deployment strengths. Anthropic London, strong alignment.

Anthropic London Research Engineer/Scientist Alignment – post-training, LLM fine-tuning, publications all align. Expression of interest format; strong Anthropic priority.

Anthropic London Senior SWE Inference – near-perfect match: sub-100ms inference, quantization, KV caching, speculative decoding are explicit candidate strengths.

Anthropic London Staff SWE Inference – Staff level + inference optimization core strength (quantization, speculative decoding, on-device). Top priority role.

Anthropic London Fellows (AI Safety) – plausible fit given research background and publications; fellowship format may not match senior IC target level.

Anthropic London Fellows (AI Security) – security focus is weaker match, but Anthropic minimum floor applies; fellowship level ambiguity.

Anthropic London AI Security Architect – security focus diverges from core ML strengths; Anthropic minimum floor. Applied AI angle offers partial overlap.

Anthropic London Research Engineer ML/RL – RL not core strength, but Anthropic London floor + LLM post-training overlap makes this worth pursuing.

Anthropic London RL Velocity – infra/velocity focus around RL; less direct fit but Anthropic London minimum floor applies. Post-training experience relevant.

Anthropic London Research Engineer Pretraining – pretraining is adjacent not core; post-training/LLM depth still relevant. Anthropic London minimum floor.

Anthropic London Pretraining Scaling – scaling focus somewhat indirect; post-training experience transfers. Anthropic London minimum floor.

Anthropic London RL Scaling Science – RL not primary strength; Anthropic London floor applies, LLM/post-training background provides partial relevance.

Anthropic London Staff SWE AI Reliability – infra/reliability angle is less direct; Staff level good fit, Anthropic London floor. Production ML system experience relevant.

Wayve Staff ML Performance Engineer Inference Optimisation – strong match: inference optimization, on-device efficiency, quantization align directly. Staff level correct.

Helsing Foundation Models Research Engineer – strong match: foundation model fine-tuning, LLM post-training expertise directly applicable. Defense domain is main caveat.

Helsing AI Research Engineer ML Engineering – LLM/post-training production engineering aligns well. Broad ML engineering scope at research org, good level fit.

Isomorphic Labs Research Scientist Applied LLMs – direct LLM application role; post-training, retrieval, fine-tuning expertise aligns well. Scientist vs. engineer title minor caveat.

Isomorphic Labs Senior SWE Inference Platform – inference platform engineering directly matches sub-100ms inference, efficiency optimization strengths. Strong level fit.