Description & Overview
Location: London, UK
Position: Hybrid (3 days onsite and 2 days remote)
Role: Full-time (Permanent Role)
We are building a world-class AI research team focused on advancing next-generation agentic systems and intent-aware learning architectures. Our mission is to bridge cutting-edge research in large language models, reinforcement learning, and alignment with scalable, real-world production systems.
You will operate at the intersection of research and product, shaping foundational capabilities in intent understanding, agent learning, and model alignment across distributed AI environments. This is an opportunity to influence AI systems deployed at global scale across diverse compute environments including edge and cloud.
Responsibilities
Define Research Agenda
- Identify high-impact research problems aligned with applied AI systems.
- Set technical direction for intent modeling, classification, and agentic learning.
- Translate business and product requirements into structured research roadmaps.
Architect Learning Systems
- Design end-to-end intent classification and agentic learning architectures.
- Lead decisions on model selection, training strategies, evaluation methodologies, and scaling approaches.
- Establish experimental rigor and reproducibility standards.
Lead RLHF & Alignment Research
- Design reinforcement learning pipelines for optimizing agent behavior.
- Define reward modeling strategies and alignment methodologies.
- Develop safety constraints and robustness evaluation frameworks.
Drive Research-to-Production Pipeline
- Ensure research outputs meet production-level reliability and latency standards.
- Partner with engineering teams on model integration, optimization, and deployment.
- Bridge experimental research with scalable infrastructure systems.
External Research Engagement
- Author internal technical papers and contribute to external publications where appropriate.
- Represent the organization at conferences, workshops, and industry forums.
Mentor & Technical Leadership
- Guide junior researchers in problem formulation, experimental design, and publication development.
- Foster a culture of technical excellence, critical thinking, and innovation.
- Provide cross-functional technical leadership across research and engineering domains.
Core Skills
- Strong foundation in deep learning and transformer-based architectures.
- Hands-on experience with PyTorch and modern NLP/LLM toolchains.
- Deep knowledge of parameter-efficient fine-tuning methods (LoRA, adapters, PEFT techniques).
- Expertise in intent classification, evaluation metrics (precision, recall, F1), and experimental design.
- Proficiency in Python and data processing/analysis tooling.
- Ability to interpret and implement techniques from cutting-edge academic research.
Bonus Skills
- Experience with reinforcement learning (PPO, DPO) or RLHF pipelines.
- Familiarity with distributed training frameworks (DDP, FSDP, DeepSpeed).
- Background in NLP tasks such as NER, semantic similarity, QA, or dialogue systems.
- Experience with experiment tracking platforms (MLflow, Weights & Biases).
- Exposure to agentic AI paradigms (ReAct, chain-of-thought reasoning, tool use).
- Prior experience in leading AI research labs or high-impact industry AI teams.
Qualifications
- PhD in Computer Science, Machine Learning, NLP, or related field (exceptional MS candidates considered).
- 5+ years post-PhD (or equivalent industry experience) in ML research.
- Strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP).
- Demonstrated track record of translating research into production systems.
- Experience mentoring researchers or leading small research groups.
What We Offer
- Career growth within a rapidly expanding AI initiative.
- Access to advanced technical training and research resources.
- Performance-based reward structure.
- Flexible hybrid work model (3:2).
- Comprehensive benefits including life insurance and mobility incentives.
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