Top Machine Learning Engineer Jobs in Denver & Boulder, CO
Develop and implement machine learning models for fraud detection, analyze large datasets, collaborate with cross-functional teams, monitor model performance, stay updated on machine learning advancements, and present reports to stakeholders.
Seeking a Staff Machine Learning Engineer with a focus on Generative AI and Recommendations to work on advancing AI product and infrastructure vision. Responsible for driving marketplace metrics, developing algorithms, and collaborating with product managers.
As a Sr. Machine Learning Engineer, you will be central to the continued development of our ML strategy while owning and designing scalable MLOps solutions to enable that strategy.
You will be a Staff Machine Learning Engineer focused on Generative AI. You will set technical direction, lead collaboration, develop algorithms, and drive product decisions through Machine Learning and Natural Language Processing. The role requires extensive experience in solving problems, technical design, and driving projects to completion.
Join the Machine Learning team at Cash App to develop models for fraud detection, customer support automation, and credit risk evaluation. Work on real-time fraud detection, proactive customer support experiences, and accessible credit solutions. Be part of a highly creative group that solves problems using first principles and deploys changes daily. Collaborate with product teams and leverage state-of-the-art algorithms and datasets to improve the lives of Cash App customers.
Design, build, and launch credit products and features for Cash App's Underwriting & Credit organization. Collaborate with cross-functional teams and work on impactful projects. Mentor fellow engineers and contribute to development capabilities.
Experiment with state-of-the-art algorithms to improve knowledge retrieval and search efficiency, develop NLP and AI models for customer support, collaborate with cross-functional teams, monitor model performance, and present reports on trends. Must be based in Pacific or Mountain Time Zone.
Work on machine learning solutions for evaluating customer cash flow risk, fraud detection, and loan optimization.
Featured Jobs
As an AI Engineer at VORTO, you will define the frontier of logistics optimization, supply prediction, and demand prediction. This role is roughly 80% software engineering and 20% data science. You will work with Go, TensorFlow, PostgreSQL, and various Google Cloud Platform tools to develop 'the brain' of the platform.
Join our team as a Staff Machine Learning Engineer at Workiva. Spearhead the architecture and delivery of cutting-edge machine learning solutions, lead projects, develop tools and systems, and ensure the reliability and support of ML infrastructure. Collaborate with product teams, provide technical leadership, and communicate complex technical issues effectively.
Seeking a Deep Learning Research Scientist to create novel deep learning systems with the capability to perform tasks that were once considered unattainable. Fully remote position reporting to the Dessa AI Research Lead. Responsibilities include designing and developing generative AI systems, improving existing deep learning systems, and advancing the research agenda through open source contributions.
As a Staff Machine Learning Infrastructure Engineer at Handshake, you will play a key role in driving the architecture, implementation, and evolution of the company's rapidly growing Machine Learning platform. Your technical expertise and leadership will help millions of students discover meaningful careers, irrespective of their educational background, network, or financial resources.
As a Senior Machine Learning Infrastructure Engineer at Handshake, you will play a key role in driving the architecture, implementation, and evolution of our ML platform. Your expertise in ML infrastructure, technological mastery, and problem-solving skills will be crucial in enhancing our data engineering tasks and deploying models. Collaboration and effective communication with both technical and non-technical stakeholders will be essential. Expertise in containerization, streaming data processing, and familiarity with large language models will be advantageous.
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