Applied Researcher 4 (AI Foundations: Recommendation Systems)
$219k - $250k • McLean, VA • Full-time
Posted 15d ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Research Engineer
About the job
Capital One is seeking an Applied Researcher to join the AI Foundations team, focusing on Recommendation Systems. This role involves creating trustworthy and reliable AI systems that are transforming the banking industry. You will partner with a cross-functional team to deliver AI-powered products, leverage a broad stack of technologies including PyTorch and AWS, and build AI foundation models through all phases of development. The position offers the opportunity to engage in high-impact applied research, translate complex work into tangible business goals, and collaborate with senior researchers on novel methods and findings.
Responsibilities
- Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products.
- Leverage technologies like PyTorch, AWS, and Huggingface to analyze large volumes of data.
- Build AI foundation models from design through training, evaluation, validation, and implementation.
- Conduct applied research to advance customer experiences with the latest AI developments.
- Translate complex technical work into clear business goals.
- Collaborate with senior researchers to prepare internal tech reports or conference submissions.
Requirements
- PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, or an M.S. with 2 years of applied research experience.
- Experience building large deep learning models (language, images, events, or graphs).
- Expertise in training optimization, self-supervised learning, robustness, explainability, or RLHF.
- Engineering mindset with a track record of delivering models at scale for training and inference.
- Experience delivering libraries, platform code, or solution code to existing products.
- Track record of high-quality ideas or improvements in machine learning, demonstrated by publications or projects.
- Ability to own and pursue a research agenda, including choosing impactful problems and autonomously carrying out long-running projects.
- Familiarity with open-source languages and cloud computing platforms.
- Deep understanding of AI methodologies.
- PhD focus on NLP or Masters with 5 years of industrial NLP research experience (preferred).
- Multiple publications on pre-training large language models (preferred).
- Experience training a large language model from scratch (10B+ parameters, 500B+ tokens) (preferred).
- Publications in deep learning theory or at major AI conferences (ACL, NAACL, EMNLP, Neurips, ICML, ICLR) (preferred).
- Experience with optimization techniques for training and inference, model sparsification, quantization, training parallelism, gradient checkpointing, or model compression (preferred).
- Experience optimizing training for a 10B+ model (preferred).
- Deep knowledge of deep learning algorithmic and/or optimizer design (preferred).
- Experience with compiler design (preferred).
- Experience with fine-tuning LLMs (Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning) (preferred).
- Demonstrated knowledge of transfer learning, model adaptation, and model guidance principles (preferred).
- Experience deploying a fine-tuned large language model (preferred).