Top Hybrid Machine Learning Engineer Jobs

Lead the development of AI/ML systems in robotics and manufacturing, designing ML architectures, and collaborating with cross-functional teams.

As a Machine Learning Engineer, you will develop AI/ML systems for robotics and manufacturing, adapt architectures for complex applications, and engage in research contributions.

As a Senior AI/ML Engineer, you will design and deploy advanced machine learning models for autonomous vehicles, collaborate with teams, mentor engineers, and drive innovation in onboard ML systems.

Responsible for designing, building, and maintaining machine learning models in production, collaborating with cross-functional teams and ensuring best practices in ML engineering.

As a Machine Learning Engineer, you'll design, build, and deploy ML applications, collaborate with teams, and optimize models and data pipelines. You'll focus on both model development and ensuring high performance in production environments.

Support development and implementation of machine learning algorithms, analyze large and sensor datasets, perform EDA and feature engineering, build ML proof-of-concept applications, and help translate models into production-ready code.

Work with R&D teams to develop, train, and evaluate ML and deep learning models for wireless communications, build data processing and feature pipelines, perform EDA and statistical analysis, integrate ML into hardware/software prototypes, automate data capture and visualization, and produce technical documentation and presentations.

Lead the development of advanced analytics and AI/ML models, involving statistical modeling and machine learning, to provide actionable insights for stakeholders and clients.

The Staff Machine Learning Engineer designs and implements infrastructure for AI workloads, collaborates with teams, mentors, and ensures code quality, focusing on machine learning and distributed systems.

The role involves developing an AI agent platform, building ML models, and enhancing user experiences with advanced algorithms. Collaboration and fast-paced execution are key.

Lead design, development, and monitoring of production ML and LLM/NLP systems. Own feature engineering, evaluation frameworks, model diagnostics, bias analysis, and cross-functional productionization with Product and Engineering.

Design, build, and deploy machine learning solutions and data pipelines; support production models; measure model impact with A/B tests; collaborate with stakeholders and communicate results.

The role involves implementing and optimizing AI solutions, analyzing data, monitoring model performance, and collaborating across teams to enhance AI-driven customer experiences.