Top Hybrid Machine Learning Engineer Jobs

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 design, architecture, and deployment of end-to-end ML solutions for onboard autonomous driving. Apply foundation-model alignment, unsupervised pre-training, imitation and reinforcement learning, and scale large-model training pipelines. Collaborate cross-functionally, mentor engineers, conduct research, and productionize foundation models for safe, reliable real-world driving.

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.

As a Staff Machine Learning Engineer, you will design and implement infrastructure for AI workloads, collaborate on GPU cluster performance, and write scalable code while mentoring colleagues.

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.

Lead the development of ML models for search and personalization at Realtor.com, driving innovation and mentoring talent in a collaborative environment.