Research Engineer

Oxford Dynamics

. Apply and adapt machine-learning and reinforcement-learning research to OD’s product roadmap, translating research direction into shippable capability.

Posted 11/11/2026full-timeOxford • United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Machine Learning and Reinforcement Learning, with a strong focus on translating research into practical applications. Proficient in model design, evaluation, and optimization, particularly in the context of LLM post-training and multi-objective policy optimization.

Highest-signal resume keywords

PhD In AI Or Machine LearningReinforcement Learning ExpertiseMulti-Objective Policy OptimisationTransformer Architectures KnowledgeHPC And CUDA Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningReinforcement LearningRLHFMulti-Objective Policy OptimisationLLM Post-TrainingFine-TuningAlignmentReward ModellingComputer VisionOpen-Source Contributions

Soft Skills

Clear CommunicationJudgement-Led Decision MakingCommercial ThinkingAbility To Operate In Ambiguity

Tools & Technologies

HPCsCUDA

Industry Keywords

AI ConferencesICMLICLRNeurIPSCVPR

About the role

Key responsibilities & impact

Apply and adapt machine-learning and reinforcement-learning research to OD’s product roadmap, translating research direction into shippable capability.
Design, train and evaluate models using reinforcement learning, RLHF and multi-objective policy optimisation while balancing accuracy, safety, latency and cost.
Work on LLM post-training, including fine-tuning, alignment, reward modelling and rigorous evaluation.
Prototype quickly and collaborate with engineering teams to harden successful approaches into production.
Define how frontier models can be evaluated and how decision-making can be audited.
Engage directly with customers and stakeholders, explain technical approaches to non-specialists and feed real-world needs into the research agenda.
Help shape Oxford Dynamics’ research and product vision and identify future capabilities to build.
Read frontier research, build real systems and connect research to customer needs and product impact.

Requirements

What you’ll need

A PhD in AI, machine learning or a closely related field, or holding a current postdoctoral research position in Machine Learning.
Strong grounding in Machine Learning and reinforcement learning, including RLHF.
Hands-on experience with multi-objective policy optimisation.
Solid knowledge of transformer architectures and LLM post-training (fine-tuning, alignment, reward modelling).
Publications at top-tier AI conferences such as ICML, ICLR, NeurIPS, CVPR, etc.
Experience using HPCs and CUDA for training large-scale models.
Ability to translate research into a product vision and carry it through to delivery.
Computer vision experience, open-source contributions, or applied research that shipped into a product are nice to have.
Commercial thinking and ability to connect research effort to product value and customer impact.
Ability to explain complex ideas clearly to technical and non-technical audiences, including customers.
Calm, judgement-led decision making and comfort operating in ambiguity with fast-moving priorities.

Benefits

Comp & perks

Rapid career progression with meaningful ownership of core systems.
Conference and tool budget.
Flexible, Hybrid working model.
Company pension (NEST, 4% employer contribution).
Private healthcare.
29 days holiday plus public holidays.