Research Intelligence
Emerging Tech Signals
Technologies tracked from arXiv, GitHub trending, and funding announcements by our Research Intelligence department — before they appear in job descriptions.
Frontier Models
Mixture-of-Experts architectures gaining traction in production deployments
LLMTest-time compute scaling emerging as training-time compute alternative
InferenceMultimodal models (vision + audio + text) becoming baseline expectation
MultimodalML Engineering
vLLM and PagedAttention driving ML infrastructure engineer demand
InferenceModel quantisation (GPTQ, AWQ, GGUF) becoming core MLOps skill
QuantisationSpeculative decoding and KV cache optimisation entering job specs
OptimisationAI Safety & Alignment
Interpretability and mechanistic analysis skills in high demand (UK AI Safety Institute)
SafetyConstitutional AI and RLHF fine-tuning skills spreading beyond frontier labs
AlignmentRed-teaming and adversarial evaluation becoming standalone job category
Red-teamingData & Infrastructure
Feature stores (Feast, Hopsworks) consolidating as MLOps standard
Datadbt + Spark + Iceberg replacing legacy data warehouse patterns in AI shops
Data EngStreaming ML with Flink/Kafka gaining traction in real-time recommendation
StreamingHigh-Impact Papers → Job Market Relevance
Scaling Laws for Neural Language Models
Foundation for all LLM hiring
FlashAttention-3: Fast and Accurate Attention with Asynchrony
Core ML infra skill
Constitutional AI: Harmlessness from AI Feedback
RLHF/alignment jobs
LoRA: Low-Rank Adaptation of Large Language Models
Standard fine-tuning approach
Mixtral of Experts
MoE architecture jobs rising
Direct Preference Optimisation (DPO)
Replaces RLHF in many pipelines
Papers surfaced by arXiv Monitor Agent. Relevance scored by semantic similarity to current UK job descriptions. Updated weekly.