Top Machine Learning Jobs in Washington, DC

Leads AI engineering initiatives for tax technology, designing and deploying machine learning solutions using Python, deep learning, and NLP. Builds data pipelines, validates models, guides experimentation, and translates tax business needs into scalable production systems. Oversees complex workstreams, directs cross-functional teams, presents technical recommendations, coaches team members, and ensures solutions meet technical, client-service, policy, and responsible AI standards.

Develops data science, business intelligence, predictive risk models, dashboards, ETL processes, and AI/ML solutions for a federal client. Uses Power Platform, SharePoint, SQL, R, Python, and related tools to automate workflows, analyze large datasets, integrate enterprise data, assess cybersecurity and business risks, and deliver actionable insights and visualizations to organizational leaders.

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.

Develop and deploy production machine learning systems for national security applications. Build Azure-based data and ML pipelines, anomaly detection and predictive models, agentic AI and RAG solutions, and cloud-native processing workflows. Support Docker and Kubernetes deployments, CI/CD, infrastructure as code, monitoring, troubleshooting, and optimization. Evaluate emerging AI technologies, communicate technical recommendations, and collaborate with government clients and cross-functional teams.

Develop and enhance client data science capabilities by analyzing complex structured and unstructured datasets, evaluating AI and machine learning models, translating findings into actionable insights, and mentoring teammates. Collaborate with defense and intelligence clients to answer analytical questions, support leadership decisions, and improve data-driven operations.

Own analytical relationships with travel technology partners, creating scalable reporting on supply, pricing, and performance. Conduct exploratory analysis, apply machine learning and statistical methods, build dashboards, automate recurring workflows with AI tools, and translate complex findings into recommendations for product, engineering, commercial, and executive audiences.

Develop and evaluate AI-driven data extraction and QA/QC processes for transportation infrastructure datasets. Collaborate with engineering, data, and research teams on LLM tool use, computer vision, model fine-tuning, benchmarking, and domain validation. Assess third-party AI tools and APIs, support scalable AWS-based ingestion solutions, and improve data quality and integrity through model evaluation and iterative process improvements.

Build and ship production AI-enabled product capabilities for transportation agencies. Partner with product, design, customer success, and data science to identify valuable AI applications, develop full-stack features, and integrate models into existing systems. Design LLM evaluations, ensure correctness, grounding, authorization, traceability, customer isolation, and prompt-injection protection. Own production reliability, latency, cost, monitoring, incident response, and continuous improvement across AI features.

Leads customer-facing deployment of an internal AI/ML Ops platform, owning solution discovery, architecture, implementation, deployment, and adoption for MLOps and GenAI workflows. Identifies platform weaknesses and drives remediation with core engineering teams. Provides technical leadership, develops production AI/ML systems, and works across Kubernetes, GPU-backed, cloud, and on-premises environments while collaborating with customers and stakeholders.

Develops and maintains data models, machine learning solutions, analytics platforms, and automated workflows for large investigative, intelligence, financial, and business datasets. The role includes data preparation, statistical analysis, data quality monitoring, requirements gathering, solution design, stakeholder collaboration, and presenting findings to technical and non-technical audiences. Supports government mission objectives and travels to field locations approximately four times annually.

Work with clients to analyze large structured and unstructured data sets, develop and evaluate AI/ML models, mentor teammates, and deliver actionable insights to leadership. Support DoW/IC customers and translate complex data into decision-ready products.

Research and develop AI-native numerical algorithms that combine machine learning with scientific and engineering solvers. Build solver-in-the-loop pipelines, learned preconditioners, AI-guided multigrid methods, differentiable solver components, and hybrid ML/numerical methods. Evaluate convergence, stability, accuracy, robustness, failures, resource use, and performance. Collaborate across NVIDIA Research, GPU computing, simulation, engineering, semiconductor, and digital twin teams while shaping the applied research agenda.

Develop data science capabilities for government and public-sector clients by analyzing complex structured and unstructured datasets, evaluating AI and machine learning models, translating findings into actionable insights, and mentoring teammates. The role involves collaborating with clients, supporting Department of War or Intelligence Community clients, and potentially applying GEOINT, LLMs, and all-source analysis expertise.

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.

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 end-to-end advanced analytics projects supporting regulatory operations and cross-market surveillance. Defines quantitative approaches, develops predictive pipelines, drives data and infrastructure availability, and delivers analytics capabilities across teams. Engages stakeholders, gathers use cases, provides research and insights, mentors analysts, and advances enterprise analytics strategy. Requires strong programming, machine learning, data mining, and analytical skills in a hybrid environment with occasional travel.

Lead complex data exploration, analytics, and AI evaluation projects supporting Department of Defense and intelligence missions. Develop scalable testing libraries, assess advanced analytics and AI system performance, and create TEVV and risk-mitigation procedures. Translate complex technical findings into clear visual and narrative products for leadership, highlighting system risks and go/no-go criteria. Work with classified networks, guide mission teams, and evaluate AI robustness, fairness, and compliance.

Develop and strengthen client data science capabilities by analyzing complex structured and unstructured datasets, applying statistics and machine learning, evaluating AI/ML models, and translating findings into actionable insights. Collaborate with clients, mentor teammates, support defense and intelligence customers, and contribute to GEOINT, LLM, and all-source analysis efforts.

Develop algorithms, evaluate AI and machine learning models, and analyze complex structured and unstructured datasets to provide objective insights for clients. Collaborate with teams and stakeholders to understand business and intelligence questions, transform disparate data into actionable information, and support data analytics, visualization, AI governance, and intelligence-related initiatives. The role requires an active TS/SCI clearance and supports public-sector and national-security clients.

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.

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.

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.

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 algorithms, systems, and analytical solutions that transform structured and unstructured data into actionable insights. Collaborate with clients and teams, evaluate AI and machine learning models, analyze complex datasets, and communicate findings that support informed decisions across government and private-sector challenges. The role may involve intelligence clients, AI governance, data visualization, and DoD analytical standards.