Top Machine Learning Jobs in Washington, DC

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads the strategy, design, and deployment of AI and machine learning solutions for tax-focused client engagements. Builds Python-based systems for text analytics, extraction, and sentiment analysis; oversees data preparation, feature engineering, model tuning, and platform integration. The Director develops client relationships, drives business development, mentors teams, promotes responsible AI adoption, and ensures governance and professional standards across initiatives.

Leads client-facing generative AI and data modernization engagements, directing solution design, delivery planning, architecture, and technical decisions. Guides teams in prompt engineering, context engineering, agent orchestration, LLM evaluation, RAG, vector databases, and AI guardrails. Oversees cloud AI implementations using Gemini, Vertex AI, AWS Bedrock, or Azure AI Services; develops Python prototypes; evaluates model performance; manages workstreams and deliverables; and mentors staff on AI methods, agile delivery, and technical problem-solving.

Designs and optimizes AI and machine learning solutions, transforms data into insights, and develops production AI systems. Responsibilities include building data infrastructure, speech recognition and NLP tools, and reusable evaluation and monitoring capabilities. The role independently tests AI systems for performance, robustness, bias, explainability, and failure modes; evaluates technical controls; documents findings; communicates recommendations to clients; and mentors junior team members.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Design and deploy AI systems and machine learning models, perform data wrangling and analysis, and integrate solutions into data infrastructure. Develop scalable AI applications, relational data systems, evaluation tools, automated tests, and monitoring capabilities. Assess AI behavior, robustness, bias, explainability, drift, hallucinations, and guardrails while evaluating technical controls and communicating findings to clients and stakeholders.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Develop and implement AI and machine learning solutions, including algorithms, data models, infrastructure, evaluation tools, automated tests, monitoring, and responsible AI controls. Lead testing for robustness, bias, drift, explainability, hallucinations, and failure modes. Manage client accounts and technical workstreams, review evidence and controls, communicate trade-offs, mentor teams, and maintain delivery and professional standards.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Leads the technical direction, architecture, and roadmap for agentic machine learning systems processing global trade data. Builds ingestion, document understanding, entity resolution, inference, evaluation, observability, and feedback systems. Adapts and post-trains LLMs, ships production AI agents, mentors engineers, sets technical standards, and partners with product, engineering, data science, and customers to deliver risk, compliance, and enforcement capabilities.

Lead the technical direction and development of production machine learning and agentic systems for global trade risk, compliance, and enforcement. Responsibilities include designing data-to-decision pipelines, adapting and post-training LLMs, building evaluation and observability systems, improving model performance, mentoring engineers, and partnering with product, customers, and cross-functional teams to ship AI capabilities.

Develops statistical analyses, predictive models, machine learning algorithms, and data visualizations from structured and unstructured data. Works with clients to identify data-driven solutions, query and transform relational data, guide teammates, and create systems that support informed decisions. The role requires collaboration in data-rich environments and may support intelligence, operations, fraud detection, or research applications.

Designs, develops, deploys, and supports cloud applications in a secure Army cloud environment. Integrates generative AI and machine learning into a large-scale document search and management platform, optimizing indexing, metadata, search relevance, scalability, and usability. Implements cloud security controls, APIs, microservices, databases, DevSecOps pipelines, and system administration processes. The role requires collaboration with government stakeholders and support for security accreditation and escalated technical issues.

Lead data science initiatives involving entity and identity resolution, knowledge graphs, information extraction, search, predictive modeling, and AI-enabled discovery. Design data architectures and machine learning workflows, establish model development and monitoring standards, guide scalable data products, evaluate emerging technologies, and brief senior leadership on AI strategy, technical risks, and investment opportunities.

Develops algorithms, predictive models, quantitative analyses, and visualizations from structured and unstructured data. The role involves data exploration, cleaning, mining, and interpretation using statistical programming, machine learning, AI, NLP, and visualization tools. The data scientist collaborates with clients to address complex questions and translate disparate data into actionable insights for public- and private-sector challenges.

Design and implement scalable data pipelines and ML algorithms to optimize StackAdapt's digital advertising platform. Collaborate with teams to solve complex problems.

Leads AI technology integration and deployment within the tax practice. Oversees data infrastructure, pipelines, modeling, quality validation, and multi-source integration. Develops Python-based analytics solutions using machine learning and natural language processing, conducts rapid experimentation, and drives data-informed innovation. Coaches and develops high-performing teams while collaborating with stakeholders and advising on strategic initiatives.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Leads the design, development, deployment, and optimization of scalable AI and generative AI solutions. Translates business needs into secure, production-ready architectures; applies foundation models, NLP, retrieval, vector search, and agentic workflows; and implements MLOps and LLMOps practices. Manages project workstreams, evaluates solution performance, ensures security and compliance, collaborates with stakeholders, and coaches junior team members.

Analyzes structured and unstructured data using statistical methods, Python, machine learning, and artificial intelligence. Develops algorithms and systems, cleans and parses data, and translates complex datasets into actionable insights for clients across public and private sectors. Collaborates with clients and team members in fast-paced environments, including work involving intelligence processes and classified data.

Develops and deploys production machine learning models and data science solutions for business, legal, investigative, and regulatory needs. Designs AI evaluation frameworks assessing bias, accuracy, reliability, and performance; analyzes structured and unstructured data; builds pipelines and testing tools; and translates legal and business requirements into technical solutions. The role collaborates with lawyers, clients, and cross-functional teams, manages concurrent engagements, communicates insights, and supports revenue-generating professional-services delivery.

Leads data science initiatives by analyzing complex structured and unstructured datasets, developing predictive models, machine learning algorithms, ETL pipelines, automated analytic software, and data visualizations. Partners with clients to define needs, translates data into actionable insights, guides teammates, and supports solutions involving AI, IoT, and intelligence analysis. Requires extensive programming, statistical modeling, data engineering, and classified-environment experience.

Develop data science algorithms, analytic software, ETL pipelines, and predictive models from structured and unstructured data. Collaborate with clients to identify needs, analyze complex datasets, and deliver objective insights for decision-making. Guide teammates and support applications across areas such as fraud detection, cancer research, and national intelligence. The role requires TS/SCI clearance and may involve intelligence data disciplines, DevOps practices, and data visualization.