Top Machine Learning Jobs in Washington, DC

Lead AI and machine learning projects, create workflows and models, analyze data from diverse sources, and present insights to non-technical audiences.

The AI & Machine Learning Director will lead machine learning projects, design data workflows, and utilize advanced data analysis and visualization to drive business insights.

The Senior Software Engineer will own large-scale data mining workflows, improve ML products through high-quality datasets, and analyze model performance across driving scenarios.

The Solutions Engineer will drive the adoption of AI-powered solutions in the electric utility sector, providing technical consulting and customer enablement. Responsibilities include gathering requirements, architecting solutions, leading RFI/RFP processes, and delivering training. Extensive travel is required for client engagement.

The Director of Engineering, Data & AI/ML will lead Bolt's data initiatives, manage teams, drive technical vision, and collaborate across functions to enhance e-commerce experiences.

The Principal Machine Learning Engineer will lead the development of AI/ML solutions to enhance user personalization within Yahoo Mail, mentor teams, and drive innovation while maintaining code quality and engineering standards.

The role involves designing scalable AI solutions, managing large-scale AI deployments, building client relationships, and delivering presentations. Requires experience in MLOps and knowledge of ML ecosystems.

As an AI/ML Ops Engineer, you will architect and operate ML pipelines, automate CI/CD processes, and collaborate with cross-functional teams to enhance engineering productivity.

Design and implement foundational machine learning systems, develop conversational interfaces, and improve existing generative AI solutions for hardware engineering.

The Machine Learning Engineer will enhance search retrieval, train ranking models, build query understanding systems, and ensure robust pipelines for Reddit's AI-driven products.

The role involves developing machine learning models for Netflix's Studio, collaborating with teams, and scaling ML solutions for data analytics.

As a ML Engineer focused on inference, you'll design and optimize deep learning models for real-time communication AI applications, collaborating with cross-functional teams and ensuring production readiness.

As a Research Intern, you will work on AI/ML projects, contributing to the development of the Nebula LLM and applying machine learning techniques to real-world problems.

Design and maintain scalable machine learning infrastructure, collaborating with data scientists, ensuring performance, security, and compliance of AI systems.

The ML Engineering Intern will contribute to the Nebula LLM development, tackle machine learning challenges, and gain hands-on experience in AI and language technology.

As a Software Engineer, you'll enhance the user experience of ML tools, optimize workflows, and collaborate with product managers on ML infrastructure projects.

As a Principal Machine Learning Engineer, you will design and develop machine learning applications, lead teams, and ensure project management excellence while engaging with clients.

Design and build infrastructure for ML model training and deployment. Collaborate with teams and monitor performance using AWS tools. Focus on reliability and efficiency in high-availability environments.

As a Machine Learning Engineer, you will develop and deploy AI/ML solutions, collaborate with product managers and engineers, and improve learning experiences.

Lead the backend engineering team for Runway's machine learning platform, focusing on building infrastructure, monitoring, and scaling production processes.

Design, implement, and maintain ML solutions using AWS SageMaker. Develop end-to-end ML pipelines and collaborate on model deployment to enhance data-driven decision-making.

Develop and optimize large-scale ML models and training pipelines for automating PCB layouts, collaborating on data pipeline design and novel architectures.

As a Senior ML Ops Engineer, you'll build ML infrastructure, implement automated deployment, optimize model serving, and ensure production performance.

As a Machine Learning Engineer, you will fine-tune transformer models, deploy machine learning solutions, and work with cross-functional teams on innovative projects.

Develop ML models and pipelines for autonomous tractor initiative, improving system safety and performance in production environments.