Top Machine Learning Jobs in Washington, DC

As an AI / ML Developer at DuploCloud, you will design and optimize machine learning models, work with large datasets, deploy models in production, develop APIs, and collaborate with cross-functional teams to create AI solutions, ensuring performance and scalability.

As a Senior Machine Learning Engineer, you will design and engineer ML systems for various industries, promote technical thought leadership within the data science team, and work across different ML approaches. Responsibilities include collaborating with stakeholders, addressing technical challenges, and deploying production ML solutions in a cloud environment.

The Lead MLOps Engineer will design and implement scalable MLOps pipelines for deploying and monitoring ML models to optimize the model lifecycle, collaborating with cross-functional teams for data ingestion, model development, and deployment, making a significant impact on advanced analytics capabilities.

The Data Scientist will design and deliver Customer Experience solutions by utilizing machine learning techniques and data analysis to solve business problems and automate processes. Responsibilities include extracting insights from customer data, implementing ML algorithms, and conducting data analysis.

As a Staff Machine Learning Engineer, you will lead the design and integration of Generative AI solutions in Firefox, collaborating across teams. You will build infrastructure for training LLMs and implement validation procedures to ensure model performance while monitoring and optimizing deployed models.

The Sr. Product Designer will shape the user experience of SentinelOne's AI cybersecurity platform by producing deliverables like wireframes and prototypes, collaborating with cross-functional teams, leading workshops for project requirements, and integrating user feedback into product updates.

The Back-End Software Engineer will develop and optimize AI RAG agents, ensure integration with OpenAI Chat API, design data processing pipelines, and enhance chatbot capabilities while contributing to system architecture and mentorship.

The Applied Scientist will leverage advancements in AI/ML, particularly Large Language Models and Computer Vision, to enhance legal and financial tech solutions. Key responsibilities include finetuning and deploying state-of-the-art models, building ETL pipelines, and collaborating with cross-functional teams to improve AI quality in production systems.

The Senior AI/ML Engineer will design, build, and deploy machine learning models and AI systems to improve clinical outcomes and health equity for low-income populations. This role involves working with a multidisciplinary team to solve key problems using AI/ML solutions, preprocessing data, testing models, and driving impactful decision-making in healthcare.

The Senior AI/ML Engineer will design and implement machine learning algorithms, maintain ML system pipelines, and integrate AI components into various applications. Collaborating with cross-functional teams, the engineer will also manage version control, implement CI/CD pipelines, and design RESTful APIs, all while working in fast-paced, regulated environments.

The ML Solutions Engineer will act as a trusted advisor to customers, facilitating technical and business discussions to enhance the use of AI applications. Responsibilities include engaging with clients, managing escalations, collaborating with the product team, and leveraging ML expertise to drive value for customers.

As a Technical Program Manager at Coinbase, you will lead machine learning focused programs, ensuring strategic alignment and roadmap delivery. You'll work with cross-functional teams, manage documentation and communication, monitor program status, and address issues proactively. Additionally, you will need to communicate effectively with both technical and non-technical stakeholders.

This role involves leading the development of AI/ML technologies and platforms, designing architecture, implementing infrastructure, collaborating with various teams, managing project roadmaps, and driving the technical vision and strategy of AI/ML within the organization. Additionally, it encompasses mentoring engineering teams and ensuring continuous improvement in quality, security, and performance metrics.