Top Machine Learning Jobs

The Research Engineer/Scientist will advance decision-making in critical industries using optimization and machine learning, collaborating with product managers and engineers.

As a Software Engineering Intern, you'll develop features for user-facing products, work on API platforms, and design data pipelines while gaining hands-on experience in AI technologies.

Design, build, and deploy AI features for claim synthesis; implement machine learning solutions and improve model performance through collaboration and evaluation.

Conduct model development and implementation for risk models, support sales initiatives, analyze transaction data with big data technologies, and promote analytics education at Visa.

Lead machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

Lead a team of engineers to enhance AI-powered search relevance through machine learning. Oversee model development, project management, and team growth while collaborating across departments.

As a Machine Learning Engineer in Fraud Risk, you'll build and maintain ML systems for fraud detection and anomaly analysis, ensuring robust performance and real-time decision making.

This role involves building and optimizing ML systems and frameworks, collaborating with teams to enhance research and development in ML models.

Lead the design, development, and deployment of machine learning models, collaborating with teams and mentoring engineers. Focus on scalable ML workflows.

Lead the design and implementation of advanced ML models for autonomous driving, collaborating with teams, mentoring engineers, and driving technical initiatives.

The Principal ML Engineer at Agero will develop and productionize a Dispatch System using ML models, optimize decision-making processes, and lead a team to improve operational performance.

As Machine Learning Product Director, define ML strategy and manage product development for Attrition Risk Insights, enhancing customer engagement through predictive modeling.

As a Principal Machine Learning Engineer, you will develop multimodal AI experiences, design agent interactions, and lead cross-team initiatives while ensuring AI safety and reliability.

Designs and develops AI tools and machine-learning models, conducts research and testing, and integrates scalable solutions while translating complex data for executive stakeholders.

The Lead Machine Learning Engineer will design and develop scalable AI solutions, perform research on ML algorithms, and oversee model training, ensuring optimal performance and integration.

Manage and grow the ML team, define goals and metrics, drive ML initiatives, build evaluation processes, and collaborate cross-functionally to enhance product delivery.

You will lead and manage a team of engineers developing machine learning solutions for perception product challenges in autonomous driving.

Develop machine learning and computer vision algorithms for automation and autonomy. Collaborate with experts, optimize solutions, and apply research insights.

Design and develop geometry processing algorithms and simulation models, manage datasets, and enhance machine learning workflows for engineering applications.

Join the Discovery ML group at Iambic Therapeutics to apply advanced ML techniques in drug discovery, collaborate with scientists, and communicate results effectively.

As an Applied Scientist, you will design and implement machine learning models, develop personalized systems, and enhance decision-making through AI technology to improve user experience and business operations.

Responsible for bioinformatics ML/AI application development in clinical settings, designing and deploying ML/AI solutions, and collaborating with multidisciplinary teams.

The Principal Machine Learning Engineer will lead ML initiatives, integrate ML services, mentor engineers, and drive ML strategy within Ibotta, ensuring scalable solutions are implemented effectively across teams.

Designs, develops, and deploys machine learning models and scalable data pipelines in production. Performs data discovery, quality analysis, model monitoring, and optimization. Collaborates with data science and business stakeholders to translate requirements, document features, and implement SDLC best practices to drive automated, scalable AI solutions.

The Lead Machine Learning Engineer will manage AI/ML project lifecycles, collaborate with teams on model optimizations, implement best practices in MLOps, and mentor juniors while ensuring model reliability.