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Top Senior Machine Learning Engineer Jobs in NYC, NY
As a Senior Machine Learning Engineer, you will develop and optimize recommendation algorithms and personalization techniques for Disney's streaming services. This role involves collaboration with cross-functional teams to achieve strategic product goals, management of algorithmic work, and maintenance of production-level systems.
The Senior Machine Learning Engineer will develop and optimize ML models for search ads, focusing on yield optimization, CTR prediction, and advertiser bidding strategies. Responsibilities include defining ML strategies, designing ML models, implementing ML pipelines, conducting A/B testing, and providing mentorship to team members.
Senior Machine Learning Engineers at Altice USA design and implement data solutions to business challenges using AI and ML techniques. They manage projects from data collection to production, collaborating with stakeholders to ensure the developed models meet business needs while identifying opportunities for innovation through data analysis and pattern recognition.
As a Sr. Machine Learning Engineer, you will analyze complex datasets, lead the development of models and algorithms, supervise data analysis, ensure software code efficiency, manage risks in machine learning predictions, and communicate insights to senior management, fostering collaboration across teams.
The Senior Machine Learning Engineer at Rokt will design and implement machine learning models to address business challenges. Responsibilities include user targeting, dynamic Ads content generation, and collaborating with product teams to architect AI-driven solutions while ensuring high-quality code and mentoring team members.
As a Senior Machine Learning Engineer, you'll design, build, and deploy machine learning applications in an Agile environment, focusing on ML architectural design, infrastructure decisions, and scaling models using cloud technologies. Responsibilities include coding, testing, optimizing data pipelines, and ensuring best practices in model governance while collaborating with cross-functional teams.
As a Senior Machine Learning Engineer, you will design, build, and implement machine learning applications. This role involves collaborating with product and data science teams, constructing data pipelines, and deploying optimized ML models. You will ensure experiences with cloud architectures and machine learning frameworks, while maintaining the production of models and application code.
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.
Featured Jobs
As a Senior Machine Learning Engineer, you will build and scale personalization and search platforms for the Max streaming app, collaborating with data science engineers and optimizing machine learning systems. You will contribute to a recommendation system, improve quality and performance, author production-level code, and support troubleshooting efforts.
The Senior Machine Learning Engineer will build and scale personalization and search platforms for the streaming app Max, collaborating with engineers and researchers to enhance recommendation systems. Responsibilities include architecting machine learning systems, improving platform performance, and contributing to code reviews and technical documentation.
The role involves building computer vision models for 3D scans, researching state-of-the-art machine learning methods, working with product and research teams to improve product lines, and implementing best practices for MLOps pipelines, all while working with large datasets.
As a Machine Learning Infra Engineer, you'll design and implement scalable MLOps pipelines, manage monorepos, develop monitoring frameworks, and collaborate with data scientists to enhance the data science platform and streamline model deployment processes.
As a Senior Software Engineer focused on Machine Learning at Celonis, you will design and develop AI/ML features and solutions, implement robust data infrastructures, and ensure data accuracy for enterprise operational data. Collaborating with various teams, you'll address customer needs and enhance product capabilities.
The Senior Staff Machine Learning Engineer will lead the design, research, and evaluation of ML models at CNN, focusing on NLP and recommendations. Responsibilities include collaborating with cross-functional teams, mentoring engineers, and driving ML projects from conception to implementation while maintaining high coding standards and fostering innovation.
The Senior Machine Learning Engineer will lead data projects to launch new machine learning products and enhance predictive features in Iterable's platform. Responsibilities include collaborating with cross-functional teams, developing machine learning pipelines, and evaluating algorithm performance using diverse datasets.
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.
The Senior Machine Learning Engineer will design and implement complex machine learning models and algorithms, conduct experiments, maintain scalable infrastructures, mentor junior members, and communicate project results to stakeholders, all while enhancing data quality in a fast-moving organization.
As a Lead Machine Learning Engineer, you will design and implement ML solutions, optimizing existing services, writing code, and managing project milestones while collaborating with engineering and product teams. This role requires leveraging ML techniques to enhance user experiences on Disney's streaming platforms.
The Machine Learning Engineer II will develop algorithms for personalization and recommendation systems for Disney's streaming services like Disney+ and Hulu. Responsibilities include algorithm maintenance, feature engineering, optimization of data pipelines, and collaboration with various teams to enhance data strategy and product features.
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 Lead Machine Learning Engineer will oversee the development and optimization of ML models, collaborating with multiple teams to enhance ad serving performance. Responsibilities include defining ML strategy, building models for yield optimization, managing A/B testing frameworks, and maintaining ML pipelines while mentoring the engineering team.
The Machine Learning Engineer will enhance Peloton's personalization through AI and ML by building and improving ML pipelines, applying advanced machine learning techniques, running A/B tests, and collaborating with various teams to deploy and monitor models. This role offers the chance to work with granular data in the fitness industry to drive engagement and user experience.
As a Staff Machine Learning Engineer at Warner Bros. Discovery, you'll architect and scale recommendation systems for the Max streaming platform, collaborating with teams to enhance personalization through advanced machine learning techniques. You'll lead projects, mentor others, and foster a data-driven culture.
As a Machine Learning Engineer at Gusto, you will build and deploy machine learning models to identify and mitigate risks. You will collaborate with engineering and product teams to create scalable frameworks and enhance modeling capabilities while communicating findings to stakeholders.
As a Lead Machine Learning Engineer, you'll design, develop, and implement machine learning applications at scale, collaborating with Agile teams. This role involves delivering ML models, integrating ML infrastructure, automating deployment, and ensuring model performance in production, while adhering to best practices in Responsible AI.
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