Top AI & Machine Learning Jobs

Extract structured facts from long, unstructured and semi-structured documents while preserving exact source citations. Validate evidence, distinguish stated totals from derived calculations, handle ambiguity without guessing, and apply established extraction methods consistently. Collaborate with entity resolution and retrieval teams to support downstream systems. The role requires strong Python and NLP or information-extraction experience, U.S. citizenship, and an active Secret clearance.

Build data preprocessing and generation pipelines, run model training and fine-tuning workflows, design benchmarking and evaluation suites, and operationalize research models into reproducible production workflows. The role collaborates with researchers, communicates metrics and results, and owns measurable machine learning outcomes from data preparation through delivery.

Design and implement machine learning, statistical, and decision-analysis models in cloud-native environments. Build large-scale Spark data pipelines, perform data cleaning and trend analysis, and translate organizational objectives into practical ML solutions. Ensure model documentation and bias testing comply with federal AI governance requirements. Collaborate with policy, technical, and program teams, contribute to open-source solutions, and manage code through GitHub.

Leads enterprise AI adoption by establishing governance, standards, intake, portfolio management, security controls, evaluation practices, and enablement patterns. Partners with Legal, Privacy, Security, Technology, FinOps, and business teams to identify automation opportunities, manage AI platforms and use cases, monitor value and risk, and support scalable solution delivery. Builds and develops a geographically distributed AI Enablement team while evaluating emerging technologies and improving enterprise AI capabilities.

Build and operate AWS-based data platforms, Java/Spring Boot services, reliable data pipelines, and AI-powered applications. Responsibilities include data ingestion and modeling, predictive ML, generative AI and RAG with Bedrock and OpenSearch, production monitoring, security, CI/CD, infrastructure as code, and integrations with databases, POS, payment, and partner systems.

Build Lighthouse AI, an intelligence engine for engineering organizations. Develop scalable ML infrastructure, LLM-powered interfaces, causal analysis models, data exploration tools, prompt engineering strategies, evaluation frameworks, and feedback loops for enterprise AI agents. Fine-tune models for performance, latency, and cost, integrate AI features into product experiences, and apply current research to business problems while collaborating across product, data, and engineering teams.

Develop ABAP integrations and AI agents for complex SAP environments. Build software across the frontend, backend, infrastructure, and AI stack; design specialized LLM harnesses; support enterprise customers; identify SAP implementation edge cases; and influence product direction. The role requires strong ABAP and SAP expertise, experience with large implementations and migrations, and pragmatic production engineering skills.

Build and operate production AI agents that complete property-management workflows across voice, SMS, web chat, and email. Integrate agents with APIs and systems of record, implement guardrails for payments and tenant data, manage stateful workflows and human escalation, and improve reliability through evaluations, tracing, regression testing, and cost and latency monitoring. Partner cross-functionally to turn operational processes into safe, measurable agent capabilities.

Executes ETL pipelines, data ingestion, analytics production runs, and machine learning inference operations for a government supply chain program. Maintains pipeline automation using Python, SQL, and orchestration tools; monitors model performance, validates data quality, produces Red Team and Blue Team analytics, documents workflows, and supports configuration releases under technical oversight.

Research Scientist developing and scaling reinforcement learning methods for frontier scientific language models. Responsibilities include designing compute-scaling experiments, improving policy optimization and credit assignment, developing adaptive sampling and curriculum strategies, studying training bias and stability, and improving training and inference efficiency. The role requires implementing and testing research ideas across complex training systems and transferring small-scale findings to large-scale runs.