Top Remote Machine Learning Jobs in Washington, DC

As an AI/ML Data Engineer, you will design and build data and ML infrastructure to create personalized student experiences, working on ETL processes, feature stores, and collaborating with product and data science teams.

The role involves applying AI/ML techniques to develop model systems for healthcare, collaborating across teams, and conducting R&D.

The Staff Machine Learning Engineer will define and build ML capabilities, design production systems, and lead ML strategy in a high-growth AI cybersecurity company.

The VP AI/Machine Learning will define and implement machine learning capabilities in a high-growth cybersecurity company, focusing on strategy, infrastructure, and building production systems. The role involves defining ML applications, data strategy, and mentoring an ML team.

The role involves defining and building ML capabilities, creating production systems, and managing data strategy while collaborating with engineers and leading an ML team.

The role involves defining and building the company's machine learning capabilities, designing production ML systems, owning data strategy, and partnering with engineers. Requires experience in ML system production and software engineering fundamentals.

Lead computer vision projects and develop ML models for power grid analysis while mentoring engineers and ensuring software quality standards.

The role involves designing and developing machine learning infrastructure and models, consulting on best practices, and building scalable services for AI integration.

As a Senior Machine Learning Engineer, you will design ML infrastructure, build AI features, and collaborate with cross-functional teams while managing large data sets.

Develop, evaluate, and deploy AI systems and machine learning models, focusing on NLP and multimodal models. Collaborate with teams and publish research.

This role involves deploying and optimizing machine learning models for autonomous vehicle motion planning, ensuring performance and reliability in production settings.

Lead a team to develop large-scale data analysis frameworks and ML models for autonomous vehicles, driving project execution and innovation.

The VP of Research, Machine Learning will set research direction for AI systems, focusing on context representation, reasoning, and evaluation frameworks while ensuring product safety and alignment.

Responsible for building and maintaining end-to-end ML pipelines and deploying production-grade ML systems. Tasks include model fine-tuning, evaluation, and collaboration with engineering teams.

As a Senior Machine Learning Engineer, you'll develop and optimize ML systems, implement pipelines, and collaborate with cross-functional teams to enhance Yahoo's data capabilities.

The role involves delivering advanced analytics and machine learning solutions, collaborating with stakeholders, and mentoring junior data scientists.

The Lead AI/ML Engineer evaluates emerging technologies, leads teams in adopting solutions, and provides technical leadership while mentoring junior specialists.

The Senior Machine Learning Operations Engineer will build and operationalize the ML Ops platform to deploy and monitor machine learning models, focusing on infrastructure, automation, and integrating ML workloads within the AWS-native architecture.

Lead technical direction for AI across products, architect RAG services, mentor engineers, and ensure compliance in AI usage.

As a Staff Machine Learning Engineer, you will design and evaluate ML models, optimize performance, and work with stakeholders to deliver AI solutions.

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.