ML Research Engineer

Aeris-UK is a small applied-AI company building AI systems for real-world applications, including autonomy, space, cyber defence, synthetic data, and language-model tools. The Machine Learning Research Engineer will investigate novel approaches and develop production-quality models, simulators, synthetic-data generators, and language-model systems, taking them from research through deployment on real and resource-constrained hardware. The role also involves customer collaboration and ownership of project workstreams.

Responsibilities:

• Writing production-quality Python the rest of the team builds on: clean interfaces, tests, pipelines that run the same way twice
• Analysing an approach before building it: reasoning out how a calibration scheme, a reward function or a model should behave, and developing from that
• Building models, simulators and synthetic-data generators for physics, sensors and engagement mechanics, because most of the data we need can’t be collected. So we make it
• Training supervised, self-supervised and reinforcement-learning models on data you’ve first had to fight into shape: scientific archives, sensor feeds and operational records that arrive nothing like a benchmark dataset
• Working out why a run stalled – a data defect, a broken reward, an unstable representation – and fixing it
• Getting models out of the simulator and onto hardware: software-in-the-loop flight stacks, bench integration, single-board computers
• Engineering the systems around language models: retrieval that surfaces the right evidence, outputs whose structure you can guarantee, evaluation that catches failures before the customer does, deployment into fully disconnected environments. Almost none of this is prompting
• Talking to customers and specialist partners directly: understanding the operational problem, presenting what you found (including when the answer is “this doesn’t work”), and shaping what happens next
• Owning a workstream: turning a vague question into concrete next steps, flagging risks early, and pulling in the team when something surprising turns up