Applied Researcher 4 (AI Foundations - LLM, Optimization and Finetuning)
$219k - $250k • New York, NY • Full-time
Posted 1mo ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
LLM Engineer
About the job
Capital One is seeking an Applied Researcher to join the AI Foundations team, focusing on LLM, Optimization, and Finetuning. This role involves partnering with cross-functional teams to deliver AI-powered products that enhance customer interactions with their money. You will leverage a broad stack of technologies, including Pytorch, AWS Ultraclusters, Huggingface, and Lightning, to analyze vast amounts of data and build AI foundation models through all phases of development. The position offers the opportunity to engage in high-impact applied research, pushing the boundaries of AI for next-generation customer experiences and translating complex work into tangible business goals. Collaboration with senior researchers for tech reports and conference submissions is also a key aspect of the role.
Responsibilities
- Partner with data scientists, software engineers, ML engineers, and product managers to deliver AI-powered products.
- Utilize technologies like Pytorch, AWS Ultraclusters, Huggingface, and Lightning to analyze data and build AI foundation models.
- Develop AI foundation models from design through training, evaluation, validation, and implementation.
- Conduct applied research to advance AI capabilities for customer experiences.
- Translate complex technical work into clear business goals.
- Collaborate with senior researchers on internal tech reports and conference submissions.
Requirements
- PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, or an MS with 2 years of applied research experience.
- Hands-on experience developing AI foundation models and solutions using open-source tools and cloud platforms.
- Deep understanding of AI methodologies.
- 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 data and inference volumes.
- Experience delivering libraries, platform-level code, or solution-level code to existing products.
- Track record of generating high-quality ideas or improving existing ML ideas, demonstrated by publications or projects.
- Ability to own and pursue a research agenda, including identifying impactful problems and executing long-running projects.
- Familiarity with LLMs, NLP, or optimization topics related to large deep learning models.
- Experience with model sparsification, quantization, training parallelism, gradient checkpointing, or model compression.
- Experience optimizing training for large models (10B+ parameters).
- Deep knowledge of deep learning algorithmic or optimizer design.
- Experience with compiler design.
- Knowledge of principles of transfer learning, model adaptation, and model guidance.
- Experience deploying a fine-tuned large language model.