Top Machine Learning Engineer Jobs
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 Machine Learning Engineer II on the ML Fraud team at Affirm, you will develop models to predict fraud likelihood using proprietary and third-party data. You will collaborate across teams to improve fraud detection capabilities, implement data pipelines, and create innovative solutions for emerging fraud patterns, impacting financial performance significantly.
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 Senior Staff Machine Learning Engineer at CNN will lead the design and evaluation of machine learning models and systems, focusing on open-source projects and methodologies. Responsibilities include mentoring engineers, collaborating across teams to drive ML projects, and applying advanced NLP techniques, while advocating for innovation based on data-driven insights.
The Staff Machine Learning Engineer will architect and scale recommendation systems for the streaming app Max, collaborating with various teams to enhance core machine learning infrastructure and projects. The role emphasizes leadership in promoting experimentation, data-driven innovation, and mentoring engineers while ensuring high standards in technical work and project execution.
As a Machine Learning Engineer, you will develop predictive models for manufacturing processes, work with data mining, clean data, and deploy machine learning models. You will collaborate closely with engineering teams and utilize both structured and unstructured data to enhance production systems.
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 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.
Featured Jobs
As a Principal Machine Learning Engineer, you will develop scalable ML systems for Roblox's Virtual Economy, overseeing design, collaborating with teams, mentoring junior engineers, and setting the direction for future ML initiatives.
As a Senior/Principal Machine Learning Engineer at Roblox, you will build models for search, recommendation, and personalization in the Marketplace. You will work with cross-functional teams, manage complex projects, establish engineering best practices, and mentor junior engineers, all while ensuring systems are scalable and reliable.
The role involves building large-scale machine learning systems focused on connecting users within Roblox, developing features like People You May Know and Friendship Score, and mentoring junior engineers. You will influence product direction and define long-term ML strategies.
As a Senior Principal Machine Learning Engineer at Atlassian, you will design and implement innovative machine learning models to enhance products like Jira and Confluence, analyze vast datasets for insights, and collaborate with cross-functional teams to foster AI integration. You'll also mentor colleagues and lead AI knowledge-sharing sessions.
The Staff Machine Learning Engineer will design, build and productionize machine learning models to tackle business challenges, collaborate with product managers and engineers, maintain high code quality, and research emerging technologies. They will also share knowledge through presentations and discussions.
As a Distinguished Machine Learning Engineer at Capital One, you will provide technical leadership, design machine learning applications, mentor engineers, and drive large-scale ML initiatives. You will collaborate with cross-functional teams, optimize data pipelines, and leverage cloud-based technologies to deliver high-performing ML solutions in the financial services industry.
As a Distinguished Machine Learning Engineer at Capital One, you'll lead the productionization of machine learning applications, guiding architectural decisions, developing models, and optimizing data pipelines. You will collaborate across teams to deliver ML solutions while mentoring engineers and applying innovative technologies in financial services.
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.
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 Vice President, you will design scalable data pipelines, deploy AI models, ensure pipeline robustness, and integrate diverse datasets while collaborating with portfolio managers and data scientists.
As a Machine Learning Engineer, you'll analyze complex datasets, develop models and algorithms, manage ML Ops, and enhance customer products. You'll communicate insights to senior management, supervise data analysis, and promote effective cross-functional collaboration within the team.
As a Machine Learning Engineer at JPMorgan Chase, you will design and develop machine learning systems, preprocess datasets, research algorithms, and support workflows for experiments and model deployments. You will transform prototypes into deployable models and develop applications based on business requirements.
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 ensure high-quality deliverables. You will also conduct research on architecture patterns and collaborate with global teams to deliver projects efficiently.
As a Machine Learning Engineer at ZS, you will build and monitor model pipelines, scale ML algorithms, implement MLOps, and write production-ready code. You will ensure high quality deliverables, collaborate with client teams, and contribute to evaluating new technologies and architecture patterns.
As a Machine Learning Engineer at ZS, you will design and implement model pipelines, scale algorithms for large datasets, develop and maintain ML engineering platforms, and ensure high-quality code for production readiness. You will collaborate with teams, participate in scrum meetings, and contribute to research on new technologies while focusing on continuous model improvement.
As a Machine Learning Engineer at ZS, you'll build and monitor model pipelines, scale algorithms for massive data sets, implement ML Ops, and produce high-quality code. You will collaborate with global teams, participate in scrum calls, and contribute to researching new technologies.
Design and develop machine learning systems, preprocess and engineer features, research and implement machine learning algorithms, support tools for machine learning experiments, transform data science prototypes into machine learning models, develop applications based on business requirements.
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