Top Remote Machine Learning Jobs in Washington, DC

You will tackle AI challenges, develop scalable software and collaborate with teams to enhance healthcare navigation using AI solutions.

Lead and develop a team of Solutions Architects within Financial Services, focusing on customer acquisition, technical evaluations, and driving sales targets, while fostering cross-functional relationships and team morale.

As a Senior Staff Machine Learning Engineer at Affirm, you will lead complex ML system design and implementation, mentor engineers, and drive innovative ML strategies for financial services.

Develop ML/RL models and infrastructure for AV simulations, optimize training loops, and support project timelines in autonomous vehicle tech.

Develop and productionize a next-gen Dispatch System, build predictive models, automate processes, and lead a small team in optimizing operations.

As a Senior Application Development Consultant, you will collaborate on solutions for business problems, utilizing various technologies and leading development projects.

The Senior Data Scientist will lead marketing efficiency efforts using data science techniques, enhance internal tools for experimentation, and collaborate with product and engineering teams.

Lead product management for an ML-driven CAD prosthetic design system, managing product lifecycle, strategy, and cross-functional collaboration.

Design and implement machine learning pipelines for fraud and payment systems, collaborating with data scientists and engineers to enhance reliability and decision-making.

The Staff Full Stack Engineer will enhance machine learning workflows at Cruise by developing UX experiences, backend integrations, and overseeing system architecture and performance metrics.

Lead the design and implementation of simulation-based validation strategies for autonomous systems, mentoring engineers and ensuring performance metrics are met.

The Senior Product Manager will define the strategy for auto loan collections and recoveries, develop customer-facing tools, track success metrics, and make data-driven product decisions to optimize business outcomes.

Lead a team of ML and DevOps engineers to build infrastructure for the ML ecosystem, including tools for experimentation and deployment. Manage a distributed team while enhancing capabilities in ML system operation and analytics production.

Develop and productionize a next-gen Dispatch System, build ML models, automate processes, and collaborate with various teams while mentoring other engineers.

The Senior Applied Scientist II is responsible for designing and implementing ML models to optimize the supply chain, collaborating across teams, and driving data-driven decision-making.

As a Senior Machine Learning Engineer, you will develop scalable ML solutions, collaborate with teams, optimize models, and implement best practices for deployments and infrastructures.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

As a Senior Machine Learning Engineer, you will design ML infrastructure, lead zero-to-one initiatives, collaborate on projects, and enhance scalability in the development of ML applications.

The Principal Product Manager will oversee AI/ML initiatives, drive product development, translate strategy into technical specs, and manage product lifecycle.

Design and deploy machine learning algorithms, develop production systems, and collaborate with stakeholders to align ML solutions with business objectives.

Lead GenAI/AI initiatives by collaborating with cross-functional teams to identify opportunities, ensure ethical practices, and develop scalable solutions. Communicate effectively with stakeholders and support successful project transitions into products.

Design and build systems for knowledge retrieval and operational plans, optimizing information retrieval processes to support real-time decision-making.

Design and develop edge AI firmware for camera systems, ensuring accurate AI algorithms, collaborating on architecture, and mentoring engineers.

The role involves designing, optimizing, and deploying machine learning models for edge environments, focusing on computer vision. The engineer will collaborate with teams to integrate models and improve inference efficiency while mentoring peers and upholding company values.

The fellow will conduct original security research on AI/ML systems, design attack methodologies, develop defensive techniques, and publish findings.