Top Remote Machine Learning Jobs in Washington, DC

As a Machine Learning Engineer in Computer Vision, you'll develop AI solutions using advanced algorithms, focusing on image classification, segmentation, and video processing tools.

Define ML strategy and build production ML systems end-to-end (data pipelines, training, evaluation, inference). Own data strategy, integrate ML with product/backend, write production-quality code, and recruit and mentor the ML team while setting technical direction.

Lead and build the companys ML function: define strategy, design production ML systems and infrastructure, own data and evaluation practices, write production code, integrate ML into products, and recruit and mentor an expanding ML team.

Lead the development of device intelligence and fingerprinting systems by designing backend services, collaborating on integrations, and applying advanced machine learning algorithms to enhance security and combat fraud.

The Machine Learning Engineer will design, develop, and deploy ML models using Google Cloud tools, focusing on Document AI and Vertex AI, ensuring scalable applications and efficient performance.

Seeking Applied Scientist to build scalable AI systems for estate planning, focusing on production-grade applications and document understanding using advanced AI models.

As a Senior Machine Learning Engineer, develop LLM evaluation frameworks and agentic AI workflows to extract insights from conversations and optimize customer experiences.

As an Associate Technical Architect in Machine Learning, you will design and develop advanced ML models on AWS, ensuring scalability and reliability while collaborating with cross-functional teams.

The Machine Learning Engineer will manage AI/ML projects on AWS, optimize data systems and databases, and enhance data architecture for predictive insights.

The Machine Learning Engineer will design, build, and deploy ML models for data-driven products, collaborating with teams to optimize pipelines and improve performance.

As a Staff Software Engineer, you will lead the development of Cake's AI platform infrastructure, focusing on building, operating, and improving ML systems. The role requires collaboration with customers to address ML challenges and enhance platform capabilities, emphasizing ownership and operational excellence.

The Principal Machine Learning Engineer will lead the design and implementation of ML models and data architectures for HelloData, ensuring production readiness and system scalability while collaborating with product teams.

The Staff Machine Learning Engineer will enhance Quora's recommendation systems, lead machine learning projects, and own end-to-end ML processes.

The role involves designing and maintaining backend systems for ML applications, working closely with cross-functional teams, and optimizing AWS services.

As a Senior ML Engineer, you'll own ML-driven features from modeling to deployment, drive innovation in generative AI, and mentor junior members.

As a Cloud Machine Learning Engineer at Hugging Face, you will build ML solutions leveraging cloud technologies, integrate models, design APIs, and contribute to documentation and community advocacy.

The Principal AI/ML Platform Engineer leads the development and delivery of ML and GenAI platforms, ensuring scalability, compliance, and performance across various workflows and products, while mentoring the engineering team.

This role involves developing and integrating AI/ML models within GCP using Python, optimizing LLM performance, and applying MLOps practices. Strong collaboration with architects is required for implementation and analysis of workflows.

Lead efforts in building and maintaining ML platforms, improve metrics pipelines, and manage data collection for perception systems. Collaborate with cross-functional teams and ensure workflow reproducibility and traceability.

As a Senior Machine Learning Engineer, you will design and train ML models for logistics, manage their production deployment, and optimize their performance while collaborating with the data science team.

The Senior ML/AI Engineer designs and implements ML/AI solutions, leading end-to-end ML/AI pipelines, collaborating with teams, mentoring, and developing infrastructure for large-scale data-driven products.

Lead definition and implementation of the companys ML strategy and infrastructure. Build end-to-end production ML systems (data pipelines, training, evaluation, serving), own data strategy and feedback loops, integrate ML with product/backend, write production-quality code, and recruit/mentor the ML team as it scales.

Lead research and development of advanced fraud detection ML models, experiment design, productionizing prototypes, and mentoring engineers.

The role involves improving recommendation systems using machine learning, developing end-to-end ML systems, and collaborating with engineers.

The Data Scientist will develop machine learning models to optimize software build processes, analyzing large datasets and collaborating with teams for improvements.