Role Description In this 3-month Internship, you will contribute your previous deal experience which will be used to help improve AI reasoning across investment banking workflows. The work involves generating, refining, and evaluating content that reflects how IB professionals think through transactions from origination and pitching through execution and close. - Draft realistic IB scenarios, prompts, and worked examples (pitch decks, comps, LBO models, CIMs, deal memos) that reflect real transaction workflows. - Evaluate AI-generated outputs against how a skilled analyst or associate would actually reason, flagging gaps in logic, technical rigour, or judgment. - Refine and rewrite model responses to improve their financial reasoning and structuring. - Partner closely with experts to translate domain expertise into training signal, giving feedback on what "good" looks like at each stage of a deal. - Identify edge cases and failure modes specific to IB workflows (e.g., sector-specific modelling conventions, regulatory nuance, negotiation dynamics). - Help document and systematise IB best practices into reusable frameworks model can learn from. Qualifications - Current student or recent graduate with a minimum of one IB internship or full-time role at a bulge bracket or elite boutique (e.g., Goldman Sachs, Morgan Stanley, J.P. Morgan, Bank of America, Citi, Barclays, UBS, Deutsche Bank, Wells Fargo, Evercore, Moelis, Centerview, Lazard, Jefferies). - Solid grounding in financial modelling, valuation methodologies (DCF, comps, precedent transactions), and deal mechanics. - High attention to detail and intellectual honesty to say "this output is wrong" even when it looks polished. - Sharp communication skills, able to explain why a piece of reasoning is right or wrong. - Comfortable with ambiguity and fast iteration. - Genuine curiosity about AI, even without a technical background. Benefits - Competitive salaries with an opportunity to join us full-time. - Modern, centrally located London office built for collaboration and focus. - Access to a range of AI tools to boost productivity and maximise results. - Tech, screens, and any equipment you need to perform at a high level. Logistics - Location: In-person (remote options available). - Start date: Immediate. - Referral bonus: For any successful referral hired into this role.

• Implement, fine-tune, and evaluate machine learning models (classic ML and generative AI) based on what each problem actually requires. • Work closely with product managers, data engineers, and domain experts to understand what teams need, translate that into a clear technical approach, and deliver something concrete. • Contribute to model design discussions with a clear point of view on tradeoffs: accuracy, latency, cost, and data availability. • Participate in code reviews as both reviewer and contributor, keeping quality and shared standards consistent across the team. • Document experiments thoroughly: what was tried, what the results showed, what the limitations were, and what should happen next. • Share technical knowledge with the team, especially when working with new methods or tools, through write-ups, short sessions, or whatever fits the situation. • Engage the Responsible AI, Data, and Platform teams early so that solutions meet the right standards before problems accumulate.

• Participate in building and training conversational AI platform using large language models • Collaborate with ML systems teams while retaining architectural ownership • Engage in deep learning and transformer foundations related work

• Set and evolve the research direction for the core intelligence, including context representation, memory, reasoning, planning, and orchestration. • Decide when to design new model architectures versus adapting or leveraging frontier open-source or commercial models. • Define evaluation frameworks that measure real-world usefulness, robustness, safety, and long-term behavior, not benchmark vanity. • Own alignment, safety, and guardrail strategy as first-class product concerns. • Guide exploration of frontier techniques such as retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, and multimodal systems. • Shape early product intelligence direction in close partnership with product and application engineering. • Set the technical bar for research rigor, judgment, and taste across the organization.