Applied Research Engineer | San Francisco

Cleo is a fast-growing fintech company on a mission to transform humanity's relationship with money through innovative technology. They are seeking an Applied Research Engineer to conduct impactful research in AI, develop prototypes, and collaborate with engineering teams to integrate research into products.

Responsibilities:

• Identify and frame ambiguous, high-value problems across conversational AI, personalisation, optimisation or generative systems
• Design rigorous experiments and evaluation frameworks
• Prototype novel architectures, training strategies, or inference approaches
• Push beyond baseline implementations and improve measurable performance
• Build working prototypes that demonstrate clear user or business impact
• Partner with ML Engineers to productionise successful approaches
• Balance scientific rigour with practical constraints (latency, cost, reliability)
• Improve how we evaluate AI systems - especially generative outputs
• Stay ahead of state-of-the-art research
• Introduce better experimentation standards and research hygiene
• Contribute to internal knowledge-sharing and technical discussions
• Define research priorities aligned to company strategy
• Mentor scientists and engineers
• Drive large AI initiatives, coordinating multiple engineers and managing stakeholder expectations

Requirements:

• MSc or PhD in Machine Learning, Computer Science, Statistics or a related quantitative field
• Strong theoretical grounding in optimisation, deep learning, generalisation, and experimental design
• Comfortable deriving, implementing, and stress-testing novel approaches
• Advanced proficiency in Python and modern ML frameworks (e.g. PyTorch)
• You design clean experiments with clear hypotheses and measurable outcomes
• You care about reproducibility, evaluation quality, and statistical rigour
• You can turn research ideas into working systems, not just papers
• You understand production trade-offs even if you're not the one deploying
• You can go deep into model internals when needed
• You can zoom out and connect research work to product impact
• You're comfortable with ambiguity and self-directed problem discovery
• You've led significant research initiatives with measurable outcomes
• You've mentored others and elevated technical standards around you
• You influence roadmaps, not just experiments