Top Machine Learning Jobs

The Staff Machine Learning Engineer will lead projects within the Square Conversational AI team, focusing on building scalable machine learning solutions. Responsibilities include collaborating with business leaders, developing ML products, mentoring team members, and overseeing the entire ML lifecycle from data collection to production.

As a Senior Machine Learning Engineer at Affirm, you will develop machine learning models for assessing creditworthiness, build training and monitoring systems, and collaborate with product and engineering teams. You'll implement data pipelines and drive innovations in credit decisioning with cutting-edge technologies.

As a Senior Software Engineer in the Central AI team, you will build and maintain core infrastructure to support machine learning engineers and data scientists. Your role involves solving complex infrastructure challenges, leading projects, and collaborating with other teams to ensure effective AI integration.

As a Senior Machine Learning Engineer, you will work on productionizing machine learning applications and systems at scale. You'll participate in the design and implementation of ML applications, collaborating with Product and Data Science teams, and focus on optimizing ML models, building data pipelines, and ensuring high performance and availability of solutions. You will leverage cloud technologies and follow best practices in responsible AI.

As a Staff Machine Learning Ops Engineer at Grubhub, you will architect and develop scalable MLOps pipelines, oversee monorepo management, implement monitoring frameworks, drive platform improvements, enhance engineering standards, and establish processes for data lineage and model management.

The Machine Learning Infra Engineer will bridge the gap between data science and production systems by optimizing models for runtime performance, designing scalable MLOps pipelines, and implementing monitoring frameworks. They will work closely with data scientists to improve workflows and ensure reliability across the system, while also developing data versioning and deployment strategies.

The Principal Machine Learning Engineer will drive the development and implementation of machine learning algorithms, collaborate with product and engineering teams, design architectures, conduct experiments, and guide emerging ML engineers, ensuring the effective integration of AI across Atlassian's products and services.

The Senior Principal Machine Learning Engineer will guide the development and deployment of advanced machine learning algorithms, collaborating closely with various teams to integrate AI functionalities across Atlassian products. Responsibilities include designing system and model architectures, conducting experiments, mentoring other ML engineers, and ensuring the effective use of AI in the product suite.

As a Machine Learning Engineer on the On-Device ML team, you will design and prototype new features, build and implement ML models, collaborate with product teams, and launch and monitor model deployments, all aimed at enhancing Grammarly's writing assistance capabilities on devices.

The Staff Machine Learning Engineer will lead the development of a marketing experimentation platform, working with cross-functional teams to design scalable ML products for optimization and ROI measurement. Responsibilities include system architecture, stakeholder collaboration, and improving ML engineering practices.

The Sr Machine Learning Engineer will develop and maintain algorithms for Hulu's recommendation system, using advanced machine learning methods. Responsibilities include feature engineering, optimizing data pipelines, and collaborating with product and data teams to enhance personalization and analysis processes.

As a Principal ML Infrastructure Engineer, you will define and drive the technical vision for Thumbtack's ML infrastructure, lead cross-functional initiatives, architect scalable ML systems, establish engineering standards, mentor teams, and align infrastructure capabilities with business objectives.

Design and develop scalable and high-quality AI/ML features and solutions based on business needs. Collaborate with cross-functional teams to implement advanced AI technologies. Build infrastructure for AI/ML models and data collection mechanisms to meet customer demands. Ensure accuracy and freshness of enterprise operational data for thousands of customers.

The AI/ML Senior Associate will develop AI/ML capabilities to enhance fraud detection, collaborate with stakeholders, implement solutions in conjunction with technology partners, and adhere to governance protocols while working in a collaborative team environment.

As a Machine Learning Engineer at JPMorgan Chase, you will build and train production-grade ML models, develop end-to-end ML pipelines, and collaborate with teams to enhance data modeling experiments. Your role will include applying machine learning algorithms, producing high-quality production code, and conducting feature engineering.

As the Vice President, Applied AI/ML Lead, you will build production-grade machine learning models and develop end-to-end ML pipelines for commercial banking applications. You'll work with large-scale datasets, applying advanced statistical methods to solve business problems using machine learning and deep learning techniques.

The Senior Lead Cybersecurity Architect is responsible for developing and updating AI technology control requirements, engineering AI-specific controls, guiding cybersecurity evaluations, providing technical guidance, and collaborating with stakeholders on AI security modifications. They aim to enhance the cybersecurity framework within the JPMC ecosystem, while promoting a culture of diversity and inclusion.