Top AI & Machine Learning Jobs

Develops and deploys data science solutions for information operations and national security customers. Responsibilities include statistical modeling, machine learning, deep learning, simulation, network analysis, multilingual text analytics, data visualization, experimentation, prototyping, customer engagement, and technical translation. The role also supports deployment in cloud, containerized, and air-gapped environments, including configuration, monitoring, troubleshooting, and maintaining customer systems. An active Top Secret clearance with SCI eligibility is required, and the position is onsite in the DC Metro area up to four days weekly.

Designs, develops, and deploys enterprise Generative AI solutions using LLMs, semantic search, and AI orchestration. Builds monitoring systems, improves AI architecture, supports prompt engineering and evaluation, reviews code, and mentors engineers. Collaborates with product leaders and stakeholders to deliver secure, scalable, customer-facing AI applications. Requires Azure OpenAI, Azure AI Search, Semantic Kernel, DevOps, CI/CD, testing, and advanced Python expertise.

Develop Python-based NLP and machine learning solutions, including text analytics, topic modeling, embedding classification, and entity extraction. Build network-analysis capabilities such as knowledge graphs and GraphRAG systems, deploy APIs, and integrate data feeds. The role supports a Navy customer and requires adapting to new technical environments with limited supervision. Candidates need at least four years of Python development experience, active Secret clearance, and Security+ certification.

Supports the rollout and adoption of AI capabilities across Capital Partners by delivering workshops, onboarding, guidance, playbooks, and learning materials. Coordinates AI pilots, product releases, change plans, and readiness activities; maintains use-case inventories and knowledge assets; gathers user feedback; monitors adoption metrics; and recommends improvements. The role also facilitates process mapping, supports responsible AI usage and governance, manages stakeholder follow-through, and helps move AI initiatives from pilot to production-ready solutions.

Develop and optimize large-scale machine learning models, open-source LLM infrastructure, and training and inference systems for advertising applications. Build distributed data and model pipelines, adapt DeepSpeed and OpenRLHF, optimize GPU/TPU serving for latency and cost, monitor model performance, and collaborate with applied scientists on research. The role focuses on recommender systems, personalization, targeting, reinforcement learning, and ad intelligence at massive scale.

Build Python data pipelines for automated perovskite solar panel characterization. Extract electrical performance metrics from raw J-V data, classify device anomalies, generate pass/fail reports, and correlate electroluminescence image metrics with IV performance. The intern will establish panel screening criteria, document results, collaborate with an engineer mentor, and present project accomplishments to leadership.

Deploys Medeloop’s AI systems into customer cloud environments, builds data and EHR connectors, and creates clinical and research workflows. Owns deployments from scoping through production, including evaluation, testing, monitoring, security reviews, and customer collaboration. Works with sensitive patient data under HIPAA, explains infrastructure and access controls to security teams, and converts custom implementations into reusable platform features.

Lead end-to-end AI/ML initiatives to improve student retention, engagement, and enrollment. Architect and operate data pipelines, feature stores, and production ML systems; deploy, monitor, and automate models; run experimentation programing and measure business outcomes. Mentor team members and set technical standards across Product, Engineering, and CX.

Leads hands-on design, development, testing, and production delivery of generative and agentic AI solutions. Builds RAG, automation, knowledge retrieval, developer productivity, and IT operations capabilities; integrates AI with enterprise systems, APIs, data, and cloud services. Ensures security, privacy, observability, evaluation, scalability, and operational readiness while mentoring engineers, documenting reusable patterns, and coordinating technical delivery across architecture, infrastructure, security, data, and business teams.

Own the end-to-end lifecycle of generative AI products across Success Academy’s school network. Translate stakeholder and business needs into AI/ML requirements, collaborate with engineering, data science, and UX teams, integrate LLMs and RAG pipelines, define evaluation metrics, run experiments, build user feedback loops, and optimize products for measurable efficiency and student outcomes. Contribute to enterprise AI strategy and manage products from discovery through launch.