Top Machine Learning Jobs in Washington, DC

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 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 workforce and healthcare data to identify trends, build predictive and machine learning models, and deliver actionable recommendations. Uses Python, Alteryx, and Power BI for analytics, visualization, and reporting. The role supports workforce strategy, employee engagement, organizational effectiveness, talent management, and healthcare cost insights. Responsibilities also include client collaboration, relationship management, team guidance, and applying risk adjustment and medical economics concepts to inform strategic decisions.

Design, create, and implement AI and ML systems for Defense and Intelligence sectors, ensuring scalability and integration into various environments.

Develop algorithms, predictive models, quantitative analyses, and visualizations from structured and unstructured data. Apply Python and statistical programming to data exploration, cleaning, mining, and analysis while using machine learning, AI, and NLP techniques. Collaborate with clients to translate data into actionable insights and support informed decisions across public and private-sector challenges.

Develop algorithms and analytical systems to interpret structured and unstructured data, apply statistical and scientific methods, and deliver data-driven insights for clients across public and private sectors. Collaborate with clients and teams in fast-paced environments, using Python and data science techniques to solve complex problems, including fraud detection, cancer research, and national intelligence challenges.

Develop algorithms and analytical systems to transform structured and unstructured data into actionable insights. Collaborate with clients to understand data-related questions, apply statistical and scientific methods, and analyze complex datasets supporting areas such as fraud detection, cancer research, and national intelligence. The role requires Python experience, data analysis skills, a bachelor’s degree, and TS/SCI clearance with polygraph.

Leads architecture and implementation of enterprise AI and data platforms supporting vector search, RAG, LLM, and Agentic AI applications. Establishes data governance, lineage, traceability, monitoring, and evaluation controls; drives technical decisions around scalability, security, reliability, and cost. Partners with engineering, data science, cybersecurity, and mission teams, communicates technical tradeoffs to executives, evaluates emerging technologies, and mentors engineers. The role requires active TS/SCI with CI polygraph clearance and extensive cloud, data engineering, MLOps, and AI platform experience.

Analyze data to identify trends and anomalies, develop machine learning and statistical models, perform data parsing and ETL with Python and SQL, create dashboards, and collaborate with data engineers. The role emphasizes NLP, LLM, and Generative AI applications, quantitative analysis, model validation, and experimentation with emerging tools for federal clients.

Leads Gibson Dunn’s firmwide legal innovation and AI strategy, including AI deployment, vendor portfolio management, custom solutions, governance, risk, training, adoption, and executive reporting. Oversees generative AI tools, RAG pipelines, integrations, evaluation standards, responsible AI frameworks, and multidisciplinary teams. Advises firm leadership on competitiveness, pricing, talent, client expectations, and regulatory obligations while building the firm’s internal AI capabilities and market presence.

Analyzes complex datasets using statistical methods, machine learning, and AI techniques. Builds and evaluates predictive models, develops reproducible analytical workflows, supports production deployment, and applies NLP, generative AI, LLMs, deep learning, and time-series methods. Partners with engineers and stakeholders to deliver mission-focused solutions, documents findings, and supports responsible AI practices including explainability, trustworthiness, and risk management.

Lead the technical direction of GenAI products and knowledge systems. Design and scale infrastructure for training, fine-tuning, serving language models and embeddings, and build retrieval, hybrid search, and RAG pipelines. Partner with applied scientists and engineering teams to rapidly prototype, productionize, and optimize reliable ML services. Establish best practices for deployment, monitoring, evaluation, latency, throughput, and resource efficiency.

Leads complex data exploration, analytics, statistical modeling, and AI evaluation projects supporting strategic intelligence. Develops scalable testing libraries, validates AI and machine learning systems, performs TEVV and risk mitigation, and communicates complex findings and system risks to nontechnical leadership. The role requires advanced analytics experience with LLMs or agentic AI, CI/CD validation, classified DoD or Intelligence Community networks, and a TS/SCI clearance with current or attainable CI Polygraph.

Reviews and evaluates advanced analytics for DIA finished intelligence. Applies mathematics, statistics, data science, artificial intelligence, and machine learning to exploratory analysis, predictive modeling, wargaming, strategy development, and complex data interpretation. Advises on data management under infrastructure constraints, produces technical narrative products, and supports classified DoD and intelligence community missions. May also review technical documentation, develop training, and support multi-INT analytics.

Develop and execute go-to-market strategies for Physical AI and robotics technologies. Identify customers, assess their needs, promote technical capabilities, drive sales opportunities, and establish strategic partnerships. Apply software engineering and AI expertise to evaluate capabilities, identify gaps, and translate technical developments into products and customer solutions. Collaborate with data scientists, engineers, researchers, vendors, and external organizations to commercialize AI-enabled offerings and expand business growth.

Perform data exploration, cleaning, analysis, and visualization; develop predictive models and algorithms on structured and unstructured data; apply ML/AI/NLP techniques to deliver client-facing solutions. TS/SCI clearance required.

Paid AI and intelligence intern supporting machine learning model development, AI agent training, data preparation, predictive analytics, NLP, AI dashboards, and custom intelligence solutions. The role involves researching emerging AI techniques, documenting model performance, and collaborating with strategists, policy experts, and creative teams on advocacy, reputation, and policy-related client work.

Designs production-ready AI architectures for enterprise customer-experience use cases. Leads presales discovery, demonstrations, prototypes, pilots, integrations, and solution validation involving LLMs, RAG, agentic AI, workflows, enterprise data, and cloud ecosystems. Establishes evaluation, observability, governance, security, scalability, latency, and cost strategies while influencing CIO and CTO stakeholders. Partners with sales, Professional Services, Product Management, and Engineering, and develops reusable enablement assets to scale field AI expertise.

Designs and implements AI solution architectures and strategies addressing client business needs and ethical considerations. Develops innovative solutions using AI, machine learning, deep learning, computer vision, NLP, and agentic AI. Leads technology assessments, evaluates alternatives, develops requirements, conducts independent research, translates complex technical topics for broad audiences, and mentors team members. Requires Python programming, modern software design knowledge, a STEM degree, and Secret clearance eligibility.

Designs, develops, and operationalizes machine learning models and advanced analytics for a 24x7 Security Operations Center. Analyzes security telemetry, builds data pipelines and feature engineering workflows, integrates analytics with SOC tools using Docker and Kubernetes, and applies threat frameworks such as MITRE ATT&CK. Provides technical leadership, mentors analytics personnel, collaborates with SOC analysts and engineers, and delivers documentation, performance metrics, and leadership briefings.

Develops advanced data analysis, visualization methodologies, intelligence support packages, and machine learning models for a federal client. Processes disparate data, identifies trends and relationships, delivers infographics and stakeholder briefings, documents technical solutions, and supports architectural reviews. The role requires Python-based data science experience, Agile development familiarity, strong communication skills, a bachelor’s degree, and an active Top Secret/SCI clearance.

Develop and evaluate supervised and unsupervised machine learning and AI models for a federal law enforcement data program. Apply NLP to extract and enrich entities from unstructured data, prepare structured and unstructured datasets, create visualizations, and communicate findings to technical and non-technical stakeholders. Collaborate with an Agile delivery team on enterprise data analytics and mission-focused modeling.

Lead forensic technology engagements by designing and deploying machine learning models, statistical algorithms, and data pipelines for digital investigations and electronic discovery. Collaborate with clients to define goals, analyze cloud and enterprise data, develop reusable automation components, communicate findings to executive audiences, and deliver training. The role also involves managing people on high-profile consulting engagements and providing client-facing technical leadership.