Applied Scientist, AI

Red Violet•Seattle, WA

1d•Hybrid

About The Position

At red violet, we build proprietary technologies and apply analytical capabilities to deliver identity intelligence. Our technology powers critical solutions, which empower organizations to operate with confidence. Our solutions enable the real-time identification and location of people, businesses, assets, and their interrelationships. These solutions are used for purposes including identity verification, risk mitigation, due diligence, fraud detection and prevention, regulatory compliance, and customer acquisition. Our cloud-native, AI-embedded identity intelligence platform, CORE™, is purpose-built for the enterprise, yet flexible enough for organizations of all sizes, bringing clarity to massive datasets by transforming data into intelligence. Our solutions are used today to enable frictionless commerce, enhance safety, and mitigate fraud and the related financial losses borne by society.

Requirements

Master’s degree in Computer Science, Artificial Intelligence, or a related field with a focus on one or more of the following: generative AI, deep learning, reinforcement learning or graph neural networks.
36 months of experience in an Applied Data Scientist, Machine Learning Scientist, or closely related role with a focus on large language models.
2 years of experience working with applied research in machine learning and/or AI, with a strong focus on deep learning, unsupervised and semi-supervised learning, and generative AI.
2 years of experience working with programming languages such as Python or C++.
2 years of experience working with the AI frameworks TensorFlow, Keras, or PyTorch.
2 years of experience working with designing and implementing deep architectures for production ML systems.
2 years of experience working with scalable AI/ML solutions delivered at the scale of national/global cohorts.
Employee must reside within normal commuting distance of the Seattle, WA office and work in the office at least 50 percent of the time.

Responsibilities

Participate in the research, development, and refinement of AI and deep learning models and algorithms to address complex challenges in fraud detection, risk management, and identity verification.
Collaborate with cross-functional teams including data scientists and search/information retrieval engineers to define project goals, requirements, and deliverables.
Apply advanced machine learning techniques to analyze large and complex datasets, extract meaningful insights, and develop predictive models to improve risk assessment, background screening, and identity verification processes.
Design and implement state-of-the-art algorithms and methodologies for entity resolution, graph database analytics, and search/information retrieval.
Stay up to date with the latest advancements in AI, deep learning, and machine learning, and proactively apply cutting-edge techniques to improve our models, algorithms, and overall system performance.
Collaborate with product management and business stakeholders to understand customer requirements and translate them into scalable and reliable AI solutions.
Present research findings, project updates, and technical advancements to internal teams and executives.
Focus on unsupervised and semi-supervised deep learning models in addition to supervised learning models.