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Top Machine Learning Engineer Jobs
The Staff Machine Learning Engineer will architect, build, and scale a recommendation system for the Max streaming app, collaborating with ML/Ops engineers, data scientists, and other stakeholders to develop personalized user experiences and improve infrastructure. The role emphasizes mentorship and driving innovation in machine learning projects.
The Staff Machine Learning Engineer will collaborate with product managers and engineers to design and productionize machine learning models, improve service performance, and maintain a high-quality codebase. Responsibilities include building machine learning solutions, conducting experiments, and sharing knowledge within the team.
The Principal Machine Learning Engineer will lead the development of predictive and optimization engines for addressable ad platforms, applying AI and machine learning techniques to innovate and enhance advertising effectiveness. Responsibilities include designing algorithms, conducting large-scale data analysis, collaborating with teams, and mentoring junior members.
The Distinguished Machine Learning Engineer at Capital One will provide technical leadership in developing and implementing machine learning applications. Responsibilities include delivering ML models, optimizing data pipelines, leveraging cloud technologies, mentoring engineers, and participating in the full ML lifecycle.
In this role, you will lead machine learning initiatives, design and build AI solutions, mentor engineers, and collaborate with business teams to implement advanced ML applications. Your focus will be on leveraging cutting-edge ML techniques and driving innovative AI use cases that improve customer experience.
As a Machine Learning Engineer II, you will develop and maintain recommendation algorithms for Disney's streaming services. Collaborating with product teams, you will optimize algorithms and manage expectations, ensuring the delivery of innovative personalized experiences for users. You will lead research and development efforts while maintaining production-ready systems and defining new personalization opportunities.
The Staff Machine Learning Engineer will lead the Conversational AI team at Square, focusing on designing and optimizing machine learning solutions across products. Responsibilities include driving ML projects from inception to production, collaborating with various stakeholders, and providing mentorship to team members.
As a Machine Learning Engineer at ZS, you will build and monitor model pipelines, work with large datasets, and deploy ML models. You will enhance ML platforms, implement ML Ops, and ensure high-quality deliverables through best coding practices while collaborating with client and global teams.
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In this role, you will build and evolve an AI platform by developing next-generation Large Language Models for enterprise applications. Responsibilities include applying ML methods to real-world problems, conducting LLM benchmarking, data collection, collaborating with cross-functional teams, and contributing to innovative developments and research publications.
The Machine Learning Engineer II, Economist will design and build machine learning solutions, work in cross-functional teams, and develop impactful solutions to support Instacart's growth. They will collaborate with engineers and product managers to understand problems, recommend solutions, and test them effectively.
The Senior Staff Machine Learning Engineer will lead the design, research, and evaluation of ML models and systems at CNN. Responsibilities include working with NLP, recommendation systems, and Large Language Models, mentoring, collaborating on ML projects, and ensuring best practices in model training and deployment.
As a Machine Learning Engineer at Machina Labs, you will develop predictive models to enhance production systems in smart manufacturing. Responsibilities include data analysis, modeling, building pipelines for ML solutions, and collaborating with engineering teams to automate and optimize manufacturing processes.
As a Staff Machine Learning Engineer on the Risk team, you will develop machine learning solutions to mitigate risk and fraud while collaborating with various stakeholders to balance business loss and customer experience. You will leverage extensive transaction data to understand customer behavior and create innovative products.
The Machine Learning Engineer II role at Remitly involves developing machine learning models for fraud prevention in financial services. The engineer will build models for transaction risk assessment, collaborate with infrastructure teams to meet performance requirements, and work on implementing new features collaboratively with scoring engineers.
As a Machine Learning Engineer at ZS, you will be responsible for building and monitoring model pipelines, enhancing ML engineering platforms, implementing ML Ops, and collaborating with teams to deliver AI solutions. You will also be involved in researching new architectures and technologies for ML applications.
As a Machine Learning Engineer at ZS, you will build, monitor, and scale model pipelines, implement ML Ops, and ensure the highest quality of deliverables. You will collaborate with teams to develop AI-enabled data products and solutions, while also researching and evaluating new technologies.
The Machine Learning Engineer at ZS will build and monitor model pipelines, scale machine learning algorithms, enhance ML engineering platforms, and implement ML Ops. Responsibilities include writing production-ready code, collaborating with teams, and researching new technologies.
As a Machine Learning Engineer at ZS, you will build and monitor model pipelines, scale algorithms for large datasets, enhance ML engineering platforms, implement ML Ops, and deliver high-quality code. Collaborating with client teams, you will research architecture patterns and technologies, contributing to the development of AI-enabled data products and solutions.
As a Machine Learning Engineer, you will develop ML solutions to enhance safety and efficiency in physical operations. You will work with cross-functional teams to build core infrastructure and services, design ML applications, and contribute to novel technology adoption.
As a Machine Learning Engineer at ZS, you will build and monitor ML pipelines, scale algorithms for large data sets, enhance ML platforms, implement MLOps, write production-ready code, and collaborate with teams to deliver AI solutions for clients.
As a Senior/Principal Machine Learning Engineer at Roblox, you will create innovative machine learning solutions for search and recommendation systems within the Marketplace. Responsibilities include building end-to-end ML systems, collaborating with teams, delivering complex projects, and mentoring junior engineers, ensuring scalable and reliable systems.
The Principal Machine Learning Engineer will build scalable ML systems for the Virtual Economy team at Roblox, focusing on ranking and purchasing transactions. Responsibilities include system design, mentorship, and team leadership, while collaborating with product and data science teams.
The Distinguished Machine Learning Engineer will lead AI/ML initiatives focused on developing production-scale machine learning applications. Responsibilities include collaborating with cross-functional teams, mentoring engineers, and implementing innovative solutions driven by a deep understanding of AI and machine learning technologies, ensuring optimal performance and high availability of applications.
As a Machine Learning Engineer II, you will develop and deploy machine learning solutions for inventory optimization, collaborating with data scientists and automating data processes. Your role involves creating scalable data analyses, managing ETL pipelines, and exploring innovative technologies to enhance our analytics capabilities.
As a Distinguished Machine Learning Engineer at Capital One, you will lead the development and implementation of machine learning applications, collaborating with various teams to solve business problems in the financial services sector. You'll guide architectural design, optimize data pipelines, and mentor engineers while applying the latest innovations in machine learning.
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