Top Machine Learning Jobs in Washington, DC

Build and deploy agentic, full-stack AI systems using LLMs, embeddings, RAG, and agent orchestration for defense and intelligence customers. Collaborate with customers, operate in classified/air-gapped environments, codify reusable patterns, and support end-to-end delivery from prototype to production.

Design, optimize, and deploy ML and LLM capabilities for resource-constrained heterogeneous edge platforms. Build efficient inference pipelines, RAG/semantic search, distributed inference orchestration, model optimization (quantization/pruning/distillation), monitoring, evaluation, and AI governance. Integrate models into production software, support multimodal and sensor analytics experiments, document results, and assist with customer demonstrations and prototypes.

Design, optimize, and deploy ML/LLM capabilities for resource-constrained edge environments. Build efficient inference pipelines, RAG/semantic search, distributed inference orchestration, model optimization (quantization/pruning/distillation), evaluation frameworks, monitoring, and production integration. Support multimodal/sensor data, documentation, demos, and responsible AI/governance.

Design, train, test, deploy, and maintain machine learning models for defense applications. Collaborate with data engineers, data scientists, architects, and product owners to implement AI use cases, guide clients on ML algorithms and tools, and drive mission-focused solutions.

Design, develop, deploy, and maintain production ML models and MLOps pipelines. Build custom AI/ML algorithms, run experiments, evaluate and optimize model performance, and integrate models with cross-functional teams for mission-driven applications in a federal environment.

Design, develop, deploy, and maintain production ML models and pipelines. Build MLOps frameworks, run experiments, evaluate and fine-tune models, and integrate AI/ML solutions with cross-functional teams to deliver mission value for federal clients.

Design, build, deploy, and maintain production ML models and end-to-end pipelines. Implement MLOps best practices (CI/CD, model versioning, registries, feature stores), monitor performance, troubleshoot model drift, and deploy/optimize solutions using cloud-native and container technologies.

The Senior Machine Learning Engineer will design and deploy AI systems, optimize production models, and ensure efficient performance in resource-constrained environments.

The Machine Learning Engineer will develop and maintain ML deployment pipelines, operationalize AI models, and ensure system reliability and scalability.

Design, build, and deploy production-grade AI/ML systems (LLMs and agents) for Defense and Intelligence clients. Implement scalable MLOps, integrate models with cloud and APIs, evaluate LLM performance, and design robust service-based architectures while meeting security clearance requirements.

Build, fine-tune, and deploy ML and LLM-driven features and agent workflows inside the NOS platform. Design evals, monitoring, and safety controls, own model quality, shape data ontology, and partner with engineers and nuclear domain experts to productionize predictive and anomaly-detection models for operational decisions.

The Machine Learning Engineer will design, build, evaluate, and deploy ML systems, collaborating on projects in safety, security, and intelligence applications, while owning projects from initial research through to production.

Own and productionize detection and tracking models: train, fine-tune, evaluate, and deploy object detection and multi-object tracking pipelines. Manage data curation and annotation, optimize models for SWaP-constrained edge platforms, collaborate with systems engineers, and work across sensor modalities (electro-optical, infrared).

Deploy and maintain state-of-the-art ML models (LLMs and vision) in production for government customers. Improve models via retraining, tuning, and architecture updates; build scalable ML infrastructure; prototype ML-driven product enhancements; work with massive datasets and multimodal fine-tuning pipelines; collaborate across research, product, and engineering. Requires active security clearance and light travel (~10%).

Lead design and delivery of production-grade ML and Generative AI platforms. Define technical vision, own full SDLC, architect scalable distributed ML systems, partner with cross-functional teams to integrate and operationalize models (RAG, agents, LLM fine-tuning) at scale.

Design, train, test, deploy, and maintain machine learning models for DoD missions. Collaborate with data engineers, scientists, and product owners to implement LLMs, agentic AI, computer vision, and generative AI solutions, and guide clients on ML tools and frameworks.