Top Senior Machine Learning Jobs in Washington, DC

Lead AI/ML consulting engagements, build and deploy machine learning models and ETL pipelines, analyze large datasets, design data architectures, deliver visualizations, drive business development, and manage client-facing teams in high-stakes situations.

As a Staff Data Scientist, you will lead initiatives to build and optimize machine learning models, collaborate across departments, and solve complex business problems using data.

Develop and deploy machine learning solutions for autonomous truck applications: EDA, deep learning model development, embedded deployment, data ingestion/curation, analytics, visualization, technical leadership, and process improvement across manufacturing and business domains.

Build and design backend services, AI agents, and machine learning models. Collaborate cross-functionally to deliver AI features and maintain code quality.

As a Senior Machine Learning Engineer at ZoomInfo, you will enhance core datasets through ML/NLP solutions, manage entity resolution tasks, and drive cross-functional impact by mentoring teams and collaborating on ML KPIs.

Build and maintain scalable ML and generative AI platforms and backend infrastructure; deploy and manage production applications; create services exposing ML/AI products; implement RAG solutions and vector databases; integrate LLM orchestration and foundation model APIs; collaborate cross-functionally and mentor teammates.

Lead design, training, fine-tuning, and deployment of ML and generative models (LLMs, diffusion). Build scalable MLOps pipelines, vectorization and data ingestion, integrate models into microservices for low-latency inference, implement evaluation/alignment/bias-mitigation, optimize models (quantization, distillation), and mentor engineers while influencing the AI product roadmap.

Design, build, and deploy high-throughput ML and LLM-powered personalization and agentic systems at consumer scale. Process petabyte-scale data, optimize real-time inference, implement resilient fallback paths, and balance latency, cost, and model quality while collaborating across engineering and product teams.

Lead architecture and delivery of scalable, production-grade ML/AI platforms and Python SDKs. Design Agentic AI and multi-agent systems, build FastAPI services, implement CI/CD, observability with Galileo, mentor engineers, and troubleshoot distributed AI systems for high availability and performance.

Design real-world ML and NLP tasks, build reference solutions and evaluation frameworks in Python, assess LLM outputs for correctness and reasoning, identify failure modes, create scoring rubrics, and collaborate with experts to improve model capabilities. Part-time, remote contractor (~20 hours/week).

As a Senior Machine Learning Engineer, you will design and maintain ML systems for content recommendations, search, and spam detection, while augmenting user experience and system performance.

The AI/Machine Learning Engineer will develop systems for automating health benefits decisions, integrating large language models with rule-driven patterns, and ensuring compliance with regulations.

Lead design, development, and productionization of advanced ML models and data pipelines. Drive architecture, experimentation, validation, and best practices while mentoring ML engineers and delivering scalable, business-driving AI solutions.

Lead design and delivery of scalable AI systems for autonomous threat detection and response. Build and productionize LLMs, graph reasoning engines, streaming feature pipelines, and orchestration frameworks. Own evaluation, reliability, and cross-functional collaboration while mentoring engineers and experimenting with RAG, tool-calling agents, and multi-modal models.

The Machine Learning Engineer will develop software solutions, optimize data pipelines, and deploy machine learning models using cloud platforms and best practices.

Lead technical execution across backend, frontend, mobile, AI, and data. Translate ambiguous product goals into technical plans, unblock hard engineering problems, coordinate cross-domain work, raise engineering quality, define hiring needs and technical evaluation standards, and build internal AI workflows to foster an AI-native engineering culture.

Lead technical pre-sales and enterprise rollouts by owning infrastructure strategy, PoCs, migrations (on‑prem to cloud), DMS/CRM integrations, InfoSec discussions, and stakeholder engagement with C-level sponsors to enable scalable deployments and upsell opportunities.

Lead program delivery for engineering teams at Arine, optimizing internal processes and ensuring timely execution of technical programs while facilitating Agile methodologies.

The AI Director will oversee ML bot development and implementation, identify AI opportunities, educate teams, and prototype AI solutions.