Role Description We are seeking an AI Research Engineer to bridge cutting-edge applied research and production engineering, designing and shipping advanced machine learning systems that solve high-impact business problems. The role blends scientific rigor with practical software engineering, requiring deep understanding of modern ML and deep learning techniques alongside the ability to build robust, scalable, and well-instrumented production pipelines. The ideal candidate stays current with the rapidly evolving AI research landscape, can critically evaluate new techniques for real-world applicability, and is comfortable operating across the full lifecycle from problem framing and experimentation to deployment and continuous improvement. Key Responsibilities - Design, prototype, and evaluate applied AI solutions across natural language, vision, recommendation, and structured data domains. - Translate ambiguous business problems into well-scoped ML formulations with clear success metrics and evaluation strategies. - Stay current with the latest research in deep learning, large language models, and adjacent areas, and assess applicability to internal use cases. - Implement rigorous experimentation workflows including baselines, ablations, and statistically sound evaluation methodology. - Build production-quality training and inference pipelines using modern ML frameworks and orchestration tools. - Collaborate with ML platform engineers to ensure efficient use of compute, storage, and accelerator resources. - Optimize models for accuracy, latency, throughput, and cost based on production requirements. - Develop tooling for dataset construction, labeling, validation, and ongoing monitoring of data quality. - Partner with product, design, and domain experts to ensure model behavior aligns with user needs and policy requirements. - Implement safety, fairness, and reliability evaluations and incorporate findings into model selection decisions. - Document research findings, design decisions, and operational characteristics clearly for both technical and non-technical audiences. - Mentor engineers on applied ML methodology, evaluation rigor, and responsible deployment. - Contribute to internal knowledge sharing, reading groups, and prototype-to-production playbooks. - Influence the broader AI roadmap based on research insight, capability gaps, and emerging opportunities. Qualifications - Master’s or PhD in Computer Science, Machine Learning, Statistics, or a closely related field; or equivalent applied experience. - Six or more years of combined research and applied ML engineering experience. - Strong proficiency in Python and modern ML frameworks such as PyTorch or JAX. - Hands-on experience training, fine-tuning, and evaluating deep learning models at non-trivial scale. - Solid grounding in mathematics, statistics, and the theoretical foundations of modern ML. - Experience taking ML models from research prototype to production with appropriate observability and safeguards. - Familiarity with distributed training, mixed-precision training, and accelerator hardware. - Strong written and verbal communication skills, including ability to explain complex methods clearly. - Demonstrated ability to read, evaluate, and adapt techniques from current research literature. - Track record of shipping impactful applied AI projects. Preferred Qualifications - Published research at top-tier AI/ML venues. - Experience with large language model training, fine-tuning, or evaluation. - Familiarity with retrieval-augmented generation, agentic systems, or multimodal architectures. - Exposure to responsible AI, model evaluation, and alignment practices. - Experience contributing to open-source ML projects. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] or contact us at (908) 505-3899. Learn more about Bright Vision Technologies at www.bvteck.com .

• Deliver robust, scalable ML solutions for AI capabilities in educational products, taking significant implementation decisions independently. • Work closely with Business Unit partners to understand their AI-related problems, help frame them clearly, and develop solutions that fit their actual needs. • Identify opportunities to reuse existing AI components and capabilities across teams. This includes use case discovery, technical solutioning, and making a credible case to partner teams for why reuse is the right path rather than building from scratch. • Review and validate the outputs of more junior scientists, providing expert-level technical feedback and catching issues before they reach production. • Shape the direction of projects within the team, bringing experience and a point of view to decisions about approach, tooling, and scope. • Propose new techniques and methods where current approaches have clear limitations, and take responsibility for evaluating and introducing them. • Mentor peers and support the growth of colleagues at earlier career stages, through code reviews, pairing, and direct feedback. • Maintain thorough documentation and uphold the technical health of deliverables over time, not just at point of delivery.

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.