Senior Applied Research Scientist
2 weeks ago
Position: Applied Researcher II
Company Overview:
At Capital One, we are dedicated to developing trustworthy and dependable AI systems that are transforming the banking landscape for the better.
For years, we have been at the forefront of the industry, leveraging machine learning to enhance customer experiences in real-time.
Our AI and ML applications are designed to simplify banking, from alerting customers about unusual transactions to providing immediate responses to their inquiries.
We are focused on building top-tier applied science and engineering teams to maintain our industry-leading capabilities, delivering innovative product experiences and robust AI infrastructure.
In this role, you will play a crucial part in harnessing the transformative potential of emerging AI technologies to redefine how we serve our customers and businesses that appreciate our offerings.
Team Overview:
The AI Foundations team is pivotal in actualizing our AI vision at Capital One. Our work encompasses every stage of the research lifecycle, from collaboration with academic institutions to the development of production systems.
We partner with product, technology, and business leaders to integrate cutting-edge AI solutions into our operations.
Key Responsibilities:
Collaborate with a diverse team of data scientists, software engineers, machine learning engineers, and product managers to create AI-driven products that transform customer interactions with their finances.
Utilize a comprehensive technology stack, including Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more, to uncover insights from vast amounts of numerical and textual data.
Develop AI foundation models through all stages of the development process, from design to training, evaluation, validation, and deployment.
Engage in impactful applied research to advance the latest AI innovations and integrate them into next-generation customer experiences.
Utilize your interpersonal skills to translate complex concepts into clear business objectives.
Ideal Candidate Profile:
The ideal candidate is passionate about analysis and innovation, committed to making ethical decisions that benefit our customers.
Innovative: You continuously explore and assess emerging technologies, staying informed about state-of-the-art methods and applications to identify opportunities for implementation.
Creative: You excel at defining and addressing complex, undefined challenges, eagerly seeking answers and sharing new ideas.
A Leader: You challenge conventional wisdom and collaborate with stakeholders to enhance existing processes, demonstrating a commitment to talent development within your team and beyond.
Technical: You are proficient in open-source programming languages and eager to expand your skills, with hands-on experience in developing AI foundation models using open-source tools and cloud platforms.
Expertise: You possess a deep understanding of AI methodologies and have experience building large deep learning models across various domains, including language, images, events, or graphs, with expertise in training optimization, self-supervised learning, robustness, explainability, and RLHF.
You demonstrate an engineering mindset with a proven track record of delivering scalable models, both in terms of training data and inference volume.
You have experience in delivering libraries or platform-level code to enhance existing products and a history of generating innovative ideas in machine learning, evidenced by first-author publications or significant projects.
You are capable of independently managing a research agenda, selecting impactful research topics, and executing long-term projects.
Basic Qualifications:
PhD with a minimum of 2 years of experience in Applied Research or a Master’s degree with at least 4 years of experience in Applied Research.
Preferred Qualifications:
PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related fields.
LLM:
PhD focus on NLP or a Master’s degree with 5 years of industrial NLP research experience, along with multiple publications related to the pre-training of large language models.
Experience as part of a team that has trained a large language model from scratch (10B+ parameters, 500B+ tokens) and publications in deep learning theory and major conferences.
Optimization:
PhD focused on optimizing the training of very large deep learning models, with experience and/or publications in model sparsification, quantization, training parallelism, gradient checkpointing, and model compression.
Experience optimizing training for a 10B+ model and deep knowledge of deep learning algorithms and optimizer design.
Finetuning:PhD focused on guiding LLMs through tasks, with demonstrated knowledge of transfer learning principles and experience deploying fine-tuned large language models.
Compensation:
Capital One offers a competitive salary range based on location, with additional performance-based incentives, including cash bonuses and long-term incentives.
We provide a comprehensive benefits package that supports your overall well-being.
Capital One is an equal opportunity employer committed to diversity and inclusion in the workplace.
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