> Markdown version of [/jobs/ext/2898491-applied-researcher-ii-ai-foundations](https://www.wearedevelopers.com/jobs/ext/2898491-applied-researcher-ii-ai-foundations). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Applied Researcher II (AI Foundations) - **Company:** Capital One Financial Corporation - **Location:** Cambridge, MA, United States - **Experience:** Experienced - **Salary:** $262,500.0 - $299,600.0 - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Amazon Web Services, Application Frameworks, Artificial Neural Networks, Computer Engineering, Data Cleansing, Data Structures, Machine Learning, Open Source Technology, Performance Tuning, Recommender Systems, Data Streaming, Cloud Platform System, Pytorch, Transfer Learning, Large Language Models, Deep Learning, Information Technology, HuggingFace - **Published:** September 14, 2026 - **Apply:** https://dejobs.org/x/x/632A01F8F3E24BEC86959043A6410A4A/job/ ## About the Role * You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it's about making the right decision for our customers. * Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them. * Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You're not afraid to share a new idea. * A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You're passionate about talent development for your own team and beyond. * Technical. You're comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms. * Has a deep understanding of the foundations of AI methodologies. * Experience building large deep learning models, whether on language, images, events, or graphs, as well as expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF. * An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes. * Experience in delivering libraries, platform level code or solution level code to existing products. * A professional with a track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects. * Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects., * Currently has, or is in the process of obtaining, PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with an exception that required degree will be obtained on or before the scheduled start date plus 2 years of experience in Applied Research or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research, * PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields * Behavioral Models * PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series) * Multiple papers on topics relevant to training models on graph and sequential data structures at KDD, ICML, NeurIPS, ICLR * Worked on scaling graph models to greater than 50m nodes * Experience with large scale deep learning based recommender systems * Experience with production real-time and streaming environments * Contributions to common open source frameworks (pytorch-geometric, DGL) * Proposed new methods for inference or representation learning on graphs or sequences * Worked with datasets with 100m+ users * Finetuning * PhD focused on topics related to guiding LLMs with further tasks (Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning) * Demonstrated knowledge of principles of transfer learning, model adaptation and model guidance * Experience deploying a fine-tuned large language model * Data Preparation * Publications studying tokenization, data quality, dataset curation, or labeling * Contribution to a major open source corpus * Contribution to open source libraries for data quality, dataset curation, or labeling ## Description * Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI-powered products that change how customers interact with their money. * Leverage a broad stack of technologies - Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more - to reveal the insights hidden within huge volumes of numeric and textual data. * Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. * Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences. * Flex your interpersonal skills to translate the complexity of your work into tangible business goals. ## Related Videos - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [How AI Models Get Smarter](https://www.wearedevelopers.com/videos/1374-how-ai-models-get-smarter) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)