Research Engineer: RL & LLM for Product Impact in Oxford

Research Engineer: RL & LLM for Product Impact in Oxford

Oxford Full-Time 60000 - 80000 £ / year (est.) No working from home possible
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At a Glance

  • Tasks: Transform cutting-edge machine learning research into impactful product features.
  • Company: Join Oxford Dynamics Limited, a leader in innovative tech solutions.
  • Benefits: Enjoy competitive pay, flexible working, and opportunities for professional growth.
  • Other info: Be part of a dynamic team driving the future of technology.
  • Why this job: Make a real difference by applying your skills to exciting projects in AI.
  • Qualifications: Experience in machine learning and a passion for research and development.

The predicted salary is between 60000 - 80000 £ per year.

Oxford Dynamics Limited is seeking a Research Engineer to bridge frontier machine-learning research with shipped product in Oxford.

You will take ideas from RL, RLHF, and LLM post-training and turn them into capabilities that improve our platforms.

You will read the literature, prototype quickly, and see your work through into production, aligning with OD’s product vision and customer needs.

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Research Engineer: RL & LLM for Product Impact in Oxford employer: Oxford Dynamics Limited

Oxford Dynamics is an exceptional employer, offering a unique opportunity to lead a talented team of engineers in the fast-paced field of AI and robotics. With a strong focus on employee growth, competitive salaries, and a flexible working environment, we empower our staff to make meaningful contributions that directly impact national security and defence. Our inclusive culture ensures that every team member can thrive and be their authentic self, making this an ideal place for those seeking a rewarding career in a mission-driven organisation.

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Contact Details:

Oxford Dynamics Limited Recruitment Team

We think you need these skills to ace Research Engineer: RL & LLM for Product Impact in Oxford

Machine Learning
Reinforcement Learning (RL)
Reinforcement Learning from Human Feedback (RLHF)
Large Language Models (LLM)
Prototyping
Literature Review
Production Deployment