Top Machine Learning Engineer Jobs

Build and own full-stack UX and backend systems for the ML lifecycle: design architecture, integrations, observability, CI/CD, and production frontend/backends. Lead technical initiatives, mentor engineers, and collaborate across teams to scale ML platform capabilities.

As a Machine Learning Engineer, you will design algorithms, maintain data pipelines, collaborate on software deployment, and ensure the scalability of ML systems.

As a Machine Learning Engineer, you will design and optimize algorithms, manage ML data pipelines, and ensure system scalability while collaborating with teams to enhance software deployment.

The Staff Machine Learning Engineer will architect and optimize data infrastructure for multimodal AI models in oncology, focusing on data management and processing pipelines.

Design, build, and operate scalable AI/ML platforms and MLOps pipelines for cloud and edge environments. Implement real-time inference, integrate AI services with OSS/BSS, manage model lifecycle and monitoring, and mentor engineers to deliver production-grade AI solutions for network optimization and customer experience.

The Senior Software Engineer will lead technical areas, manage AI/ML projects, develop model services, APIs, and collaborate on design and UX to enhance cloud-based AI solutions.

The Senior Software Engineer will lead technical projects, build AI/ML features, improve APIs, and collaborate on design, ensuring efficient operational practices.

As a Senior Software Engineer, you will build AI platform features, provide technical leadership, drive projects, and maintain high-performance systems primarily using Python and Go.

The Senior Software Engineer will lead projects focused on building an AI platform for managing models, collaborating on features, and maintaining high performance in a scalable environment.

The Senior Software Engineer will provide technical leadership, build and scale AI/ML systems, maintain APIs, and collaborate on design while independently shipping product features.

The Machine Learning/MLOps Engineer will develop automated AI systems for enhancing business processes across maritime production, focusing on model operationalization and workflow automation.

Lead the design and deployment of an AI-enabled platform for digital shipbuilding, focusing on operationalizing advanced models and ensuring system integration across various teams.

Lead development of scalable ML infrastructure, focusing on compute for simulation, data labeling, and data generation. Collaborate with ML engineers, set technical roadmaps, ensure system reliability, and research advanced computing techniques.

The Senior ML Infrastructure Engineer will design, implement, and optimize computing platforms for simulation workflows, ensuring high reliability and performance. Responsibilities include leading technical decisions, collaborating with ML engineers, and enhancing system efficiency.

Design, build, deploy, and maintain AI/ML solutions (including LLMs and Generative AI) for provider-facing products. Lead conversational AI/chatbot initiatives, integrate AI into cloud-based services, ensure responsible and compliant AI use, and collaborate with product and engineering teams to deliver scalable, production-quality solutions.

Lead development of computer vision and 3D generative ML models to extract structure from 3D scans. Research SOTA methods, work with large datasets, build evaluation metrics, implement MLOps best practices, and collaborate with product and research teams to deliver features.

Design and build computer vision and 3D generative models to extract structure from 3D scans, research SOTA methods, work with massive datasets, develop evaluation metrics, implement MLOps best practices, and collaborate with product teams to deliver features.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

Design, develop, validate, and deploy end-to-end AI/ML solutions for aerospace systems. Lead data collection/preprocessing, model training and optimization, safety risk management, production integration, monitoring, and cross-functional collaboration while ensuring ethical, regulatory, and performance requirements.

Join the Inference team to implement features and improvements for model serving on a GPU platform, focusing on performance and reliability.

Design, implement, and scale AI/ML systems and data pipelines for healthcare applications. Own model inference, deployment, monitoring, and reliability; collaborate with cross-functional teams to deliver production ML solutions using cloud and distributed computing.

Design, implement, and operate production AI/ML systems for healthcare. Own inference and delivery of ML insights, scale and monitor models, build and optimize data pipelines, apply MLOps practices, and partner with cross-functional teams to integrate AI into products.

Build and optimize machine learning models for 3D dental applications, research state-of-the-art methods, and implement MLOps best practices.

Lead the development of Computer Vision and Machine Learning projects at Dandy, focusing on 3D geometric problems and automation in dental manufacturing.

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