Top Machine Learning Engineer Jobs

As a Senior Machine Learning Engineer, you'll design and deploy ML models, build cloud-native solutions, and mentor junior engineers within healthcare technology.

The Staff Machine Learning R&D Engineer will innovate machine learning algorithms for spatial computing, collaborate with teams, and translate research into production solutions.

Develop and maintain ML platforms, collaborating with teams to implement scalable solutions and mentor others in AI and ML technologies.

The role involves developing and deploying machine learning models for forecasting within the Camus platform, requiring collaboration and technical depth in ML.

Design and develop AI capabilities and scalable microservices. Collaborate to integrate AI models and ensure performance in cloud environments.

Join Nextdoor's machine learning team to build data-intensive products, develop ML models, analyze datasets, and enhance user experiences. Collaborate closely with product and data science teams.

Develop and improve machine learning models for Pinterest's various product surfaces, utilizing deep learning and advanced data processing.

Develop and execute ML technology stack vision for Ads, improve ML models, and enhance content understanding using LLMs in a collaborative environment.

The Sr Software Engineer will optimize and deploy ML models on embedded hardware, ensuring performance and reliability, while collaborating with cross-functional teams.

As a Senior Machine Learning Engineer, you will design and implement systems for ML and NLP, optimizing models and tools for Clover's healthcare solutions.

The role involves developing ML models, evaluating technologies, collaborating with teams, and enhancing Xometry's AI capabilities.

Lead initiatives to enhance the ML platform, mentor junior engineers, build scalable ML models, and ensure code quality while driving innovation in a startup environment.

Join Xometry as a Senior Machine Learning Engineer to develop and implement machine learning models, enhancing predictive capabilities and leading technological evaluations for innovation.

Lead the development of AI systems for an automation platform, focusing on design, implementation, and evaluation of AI/ML projects in financial services.

The Senior Machine Learning Engineer will lead the development of a GenAI platform, enhance AI infrastructure, mentor junior engineers, and drive project ownership in a startup environment.

Build and operate large-scale batch and streaming data pipelines and real-time feature infrastructure to support production ML. Develop ML-adjacent services, ensure reliability/observability, participate in on-call and incident response, and collaborate with ML, data, and platform teams to meet latency, availability, and data quality requirements.

As an MLOps/ML Platform Engineer, you will build and manage ML systems, optimize workloads, and ensure production model reliability while collaborating across teams.

Seeking a Senior Machine Learning Engineer to design and implement ML systems, optimize deployment, and collaborate with data teams, focusing on scalability and performance.

Lead the design, development, and deployment of scalable machine learning and AI systems for enterprise clients, guiding architecture and mentoring engineers.

Design and develop machine learning infrastructure, enhancing core components for model training, monitoring, and serving. Collaborate with ML engineers on robust pipelines.

The AI/Machine Learning Engineer II will design and develop models to analyze data for health monitoring devices, focusing on diagnosis and prediction using various algorithms.

The Sr Engineer will develop AI/ML models for health monitoring devices, focusing on algorithm development, data analysis, and cross-functional collaboration.

The Senior Machine Learning Engineer will design innovative machine learning models for drug discovery, leveraging large-scale single-cell datasets and collaborating with ML Scientists to develop optimized models.

The R&D Engineer will build and refine software solutions for defense use cases, prototype systems, and convert stakeholder feedback into technical requirements. Collaboration with engineering teams and customer-oriented engagements are key responsibilities.

Lead experimental ML initiatives: prototype and scale multimodal models, design agentic workflows, improve ML infrastructure and governance, mentor teams, translate research into business value, and drive responsible AI adoption.