Top Fintech Machine Learning Jobs in New York City, NY
As a Senior Machine Learning Engineer, you'll join an Agile team to design, build, and implement machine learning applications at scale. Responsibilities include model development, optimizing ML models, collaborating with cross-functional teams, and maintaining production models, all while adhering to best practices in ML engineering.
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As a Senior Machine Learning Engineer at Canoe Intelligence, you will develop NLP algorithms, train large language models, and work on data preprocessing with cross-functional teams. Your role involves driving innovation in data processing pipelines and mentoring junior engineers.
The Senior Machine Learning Engineer will build scalable machine learning systems, enhance entity resolution algorithms, and develop ML infrastructure. Responsibilities include designing reliable ML solutions and collaborating with cross-functional teams to optimize performance and testability.
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As an AI Research Scientist, you will lead innovative research in machine learning and data science, conducting large-scale experiments, developing advanced deep learning models, and publishing findings. You will collaborate with teams to refine methodologies and contribute to evolving research practices, applying the latest theoretical advancements to enhance technologies and products.
As a Senior Software Engineer on the Predict team, you'll develop scalable, fault-tolerant systems for machine learning, optimize low latency applications, and collaborate with cross-functional teams to enhance risk management tools. You'll also participate in the on-call rotation and stay updated on industry developments.
The Data Scientist III (ML Engineering) will develop and optimize machine learning solutions to enhance and differentiate Cedar’s healthcare payment platform. Responsibilities include data analysis, feature engineering, and model performance monitoring, while collaborating with other teams to communicate insights and improve patient engagement.
As a Data Engineer, you will own the architecture, management, and deployment of the Arlo underwriting API, collaborating closely with actuaries to refine models and ideate strategies. Your responsibilities include setting up data pipelines, implementing reporting tools, evaluating new data sources, and deploying models into production.
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