Top Machine Learning Engineer Jobs

As a Principal Machine Learning Engineer, you will architect AI solutions, collaborate with scientists, and optimize models for edge deployment. Your work enhances public safety applications.

The Machine Learning Engineer will apply data science and machine learning practices to solve problems, develop prototypes, and communicate insights to stakeholders in a dynamic environment.

The role involves training and developing large-scale image and video models, optimizing data pipelines, and conducting experiments in a high-performance computing environment.

The ML Engineer will architect and build personalization and recommendation systems, design user preference algorithms, collaborate with teams, and improve systems from research to production.

The Machine Learning Engineer II will enhance ML capabilities for autonomous vehicles, train and optimize models, and ensure high performance through collaboration with cross-functional teams.

The ML Engineer will develop and advance multimodal models for robot perception and behavior, integrating these systems into hardware while collaborating closely with robotics engineers.

Contribute to ML system design, development, and evaluation for recommendations, collaborating to integrate and A/B test signals, promoting best practices.

As an ML Engineer, develop and deploy advanced ML solutions for OCR, document processing, and voice technologies, collaborating with teams to optimize models for performance.

The role involves designing and improving AI/ML platforms and operational infrastructure, collaborating with engineers and product managers, and mentoring teams on MLOps best practices.

As a Senior ML Infrastructure Engineer, you'll design and build scalable platforms for ML inference workflows, collaborating with teams to optimize model serving and enhance system reliability.

Lead research on machine learning validation for robotics and autonomy systems, focusing on simulation, uncertainty modeling, and creating functional prototypes for testing.

Lead development of advanced recommendation systems, driving ML innovation, maintaining ML pipelines, and collaborating across teams to enhance user engagement.

As a Sr Lead Machine Learning Engineer, you'll design, develop, and implement machine learning applications, collaborating with cross-functional teams and ensuring high performance and governance of ML systems.

Design and implement technology architecture and solutions for managing data from various sources, utilizing AI and ML techniques to improve data quality and generate insights.

Design and implement production-grade ML systems for ICS/OT cybersecurity, build and optimize models (including NLP/LLMs), develop data pipelines and MLOps workflows, collaborate with data teams to deploy and monitor models in cloud and on-prem containerized environments, and troubleshoot production performance and resource issues.

The role involves engineering and deploying AI agents, collaborating with teams on solutions, and managing data flows. The position requires deep expertise in ML technologies and frameworks.

The role involves training and deploying ASR models, improving pronunciation feedback, measuring performance, and expanding ASR systems for language learning.

As a Senior Applied ML Engineer at Macroscope, you will design, build, and optimize ML/AI systems, leading the AI Code Review process and ensuring integration with backend systems.

Develop and implement AI and ML systems for Defense and Intelligence sectors, focusing on deployment, scalability, and integration of technologies.

Develop and improve machine learning models and systems for clinical outcomes. Lead ML initiatives, mentor teams, and establish best practices in AI. Collaborate across engineering, product, and design for data-driven solutions.

As a Machine Learning Engineer 1, you will develop ML models for search, recommendations, and NLP tasks, building data pipelines and contributing to real production systems.

Develop and implement machine learning algorithms for defect detection in high-resolution images. Optimize real-time performance and evaluate model efficiency.

Lead the development of production-grade machine learning systems, build ML pipelines, and optimize models for deployment and performance.

As a Machine Learning Engineer, you will design and build AI capabilities, develop scalable ML infrastructure, and collaborate on production systems in the real estate domain.

Develop and train models for sensor-level scenario generation using deep learning and multi-sensor fusion for autonomous vehicle perception at Zoox.