Top Machine Learning Jobs in Washington, DC

Design, build, and scale ML and Generative AI systems. Develop applications using ML architectures and monitor model performance.

The Staff Machine Learning Engineer will lead ML initiatives, develop AI systems, optimize pipelines, and mentor team members to automate healthcare processes.

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

The Machine Learning Engineer will execute the full ML lifecycle, collaborating on model implementation and improving customer experiences through data-driven insights.

Lead the design and operation of Twilio's ML and data platform, architecting cloud-native pipelines and implementing MLOps best practices while mentoring engineers and collaborating across teams.

As an AI/ML Architect, you will define scalable AI systems architecture, mentor teams, guide clients, and ensure ethical AI practices.

Lead research on humanistic AI for autonomous systems. Collaborate with teams, design experiments, and publish findings in top-tier publications.

As a Machine Learning Engineer, you'll develop and deploy ML models for medical imaging, collaborate with teams, and optimize for production environments.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

The Principal Machine Learning Engineer will develop multimodal technologies, mentor engineers, and drive product innovation in agentic AI systems.

Lead the strategy and implementation of AI solutions in R&D, driving adoption and improving operational efficiency through advanced analytics. Collaborate with cross-functional teams to design AI/ML models and evaluate new technologies.

As a Lead Machine Learning Engineer at Root, you'll build scalable ML systems, partner with data scientists, and improve operational practices for customer lifetime value modeling.

Lead the engineering organization for data and AI platforms, making architectural decisions and driving product vision while managing teams and aligning with business priorities.

The Senior AI Engineer will develop AI solutions for incident management, collaborate cross-functionally, and enhance observability tools through iteration and ownership.

The role involves applying established ML techniques to cybersecurity data problems, integrating ML outputs into data pipelines, and ensuring model reliability. Collaboration with engineers and product teams is key, along with strong communication skills and understanding of data flow.

The role involves leading ML client engagements, shaping strategies, advising on ML architecture, and influencing project outcomes, while guiding teams and driving business value.

As a Senior Machine Learning Engineer, you will develop and deploy ML models for health data analysis, collaborating with cross-functional teams to ensure reliability and regulatory compliance.

Design and implement ML infrastructure for scalable deployment, collaborate across teams, optimize ML systems, and enhance tooling.

As a Machine Learning Engineer, you will design, build, and deploy ML models for underwriting at Stripe Capital, ensuring performance while collaborating with multiple teams.

The role involves designing, training, and evaluating ML models, optimizing performance, and collaborating with teams to implement AI technologies in products.

The Machine Learning Systems Engineer will optimize ML training systems, improve data pipelines, and develop GPU kernels to enhance scalability and performance.

The AI/ML Engineering Manager leads a team of ML engineers and architects, managing hiring, performance, and customer engagements while providing technical guidance in ML operations and architecture.

Develop and deploy production-grade AI/ML models, design end-to-end pipelines, and ensure compliance while collaborating with stakeholders in a federal environment.

Lead a team of data scientists developing ML-driven products, managing infrastructure, training models, and ensuring production quality while collaborating with senior leadership.