Top Remote Machine Learning Jobs in Washington, DC

The Implementation Specialist (CRM) will deliver CRM solutions, customize workflows, train staff, optimize processes, and ensure client engagement for dealer partners.

Manage technical programs for generative AI video models, translating research into milestones, coordinating teams, and ensuring successful execution across all phases.

Lead the ML Data Enablement team to define strategy and execution for accelerating third-party and internal data onboarding, enforce data quality and SLAs, build metadata and lineage standards, and deliver scalable data infrastructure and workflows to reduce time-to-production for ML models.

The role integrates capabilities across IDS to drive excellence, facilitates learning programs, ensures alignment with objectives, and enhances decision-making in the pharmaceutical field.

The Enterprise Account Executive will drive revenue by engaging with enterprise insurance clients, managing the sales cycle, and fostering strategic relationships with executives, leveraging Vellum's data analytics platform.

As Director of AI/ML, lead the ML organization, set technical vision, mentor a high-performing team, and contribute to model development and production deployment.

The Manager of RevOps/Yield analyzes patterns and data to optimize ad operations, manage vendor relationships, and oversee a remote team for revenue generation.

The Senior Product Manager will own the roadmap and execution of a real-time data platform, leading product strategy, discovery, and commercialization while collaborating with cross-functional teams to enhance data-driven insights and product performance.

As a Staff Machine Learning Engineer, you will optimize Quora's ad products by applying machine learning algorithms, taking ownership of systems, and collaborating across teams.

Lead the Applied ML team's experimentation efforts, mentoring scientists and engineers to improve AI-driven methodologies and collaboration across various product areas.

You will lead technical strategy and engineering for ML-powered messaging features at Airbnb, integrating solutions and collaborating with teams to enhance user communication. Responsibilities include system design, mentoring, and establishing engineering standards.

The AI Forward Deployed Engineer designs and implements AI solutions, collaborating with stakeholders, enhancing reliability, and producing reusable resources. Responsibilities include diagnosing AI solution issues and engaging with finance and data teams.

Partner with CV researchers to productionize models, optimize training and real-time inference performance, design reusable abstractions, and collaborate with MLOps/backend teams to evolve the CV inference platform for scale and reliability.

The Staff Machine Learning Engineer will lead AI initiatives, architect LLM systems, design evaluation frameworks, and mentor engineers.

The Staff Advanced Analyst will drive data analytics to improve CS Safety experiences, influence product decisions, and build measurement tools using data-driven insights.

Lead the Marketplace engineering team to enhance the renter experience by developing scalable systems, managing teams, and collaborating with cross-functional stakeholders to drive product strategy.

As Engineering Manager for the Marketplace team, lead full-stack engineers, drive strategic vision, manage team processes, and enhance user experience using AI and ML innovations.

The Customer Success Manager will manage post-sales relationships, drive customer engagement and ROI, and advocate for client needs within the Buzz Solutions platform.

The role involves leading a team to manage data strategies and analytics for Netflix Games, requiring expertise in data science, business analytics, and team management.

As a Solutions Engineer, you will drive sales growth, support partnerships, and deliver AI-driven solutions while collaborating with various teams.

The Product Manager will lead the development of data-driven product capabilities at Axuall, ensuring features are delivered on time and meet customer needs through collaboration with various teams.

Design, build, and scale production ML systems (including LLMs) to automate customer support and compliance workflows. Lead technical roadmaps, write high-quality Python code, mentor engineers, ensure secure, scalable architectures, and resolve complex reliability issues.

Design and deploy AI-powered solutions using ML techniques. Collaborate with teams to develop models and optimize architectural decisions.

The Senior Machine Learning Engineer will develop high-performance services, innovate ad technologies, and collaborate with teams to optimize ad campaigns through AI and ML solutions.

Lead the Machine Learning Engineering team at AKASA, focusing on improving ML capabilities, model deployment, and infrastructure development, while managing and mentoring engineers.