Senior Research Scientist (Must be based in UK)
- Conversational reinforcement learning
- Streaming and continuous turn-taking
- Audio-native LLMs
- Distillation of reasoning models
- Long-context
- Efficient adaptation
- Lead and execute complex research projects with clear business impact.
- Design and implement novel post-training strategies including preference tuning, reward modeling, and synthetic supervision.
- Develop innovative model architectures and training approaches for conversational AI, including speech-aware and multimodal models.
- Conduct empirical studies to assess model performance in live deployments and iterate quickly based on real-world data.
- Generate, collect, and annotate training data - including synthetic and real-world conversational datasets - with an eye for quality and bias mitigation.
- Design robust evaluation metrics and benchmarks for LLM-based assistants in customer service domains.
- Work closely with engineering and product teams to integrate research into production environments.
- Collaborate with legal and compliance teams to ensure responsible use of data and models.
- PhD in Machine Learning, Natural Language Processing, Computer Science, or a related field.
- 5+ years of hands-on experience in deep learning.
- Proven track record of research innovation, including published work or deployed systems.
- Strong programming skills in Python and deep learning frameworks like PyTorch.
- Demonstrated expertise in at least one domain area such as reinforcement learning, conversational AI, audio modelling, or LLM alignment.
- Experience leading projects end-to-end, from ideation to deployment.
- Excellent communication skills with the ability to write clear technical documents and explain complex concepts to diverse audiences.
- Comfortable working in ambiguity and driving clarity through experimentation and data.
- Experience with speech technologies such as ASR and TTS.
- Familiarity with cloud environments (AWS, GCP, Azure).
- Exposure to RLHF, reward modelling, or human preference data collection.
- Prior work on real-time systems, streaming inference, or memory-efficient model deployment.
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