Top Artificial Intelligence Jobs

Own and grow an enterprise sales territory across North Texas and Oklahoma. Develop executive customer relationships, prospect for new business, expand strategic accounts, sell HPE technology solutions, lead complex consultative sales cycles, manage pipelines and forecasts, achieve quotas, and collaborate with sales engineers, channel partners, and cross-functional teams.

Develop and integrate AI/ML solutions for geospatial intelligence, including spatio-temporal modeling, data fusion, semantic enrichment, trajectory analytics, and remote sensing. Engineer multimodal data pipelines, evaluate models, prototype applications, and implement solutions across hardware and software stacks. Provide technical expertise, mentorship, strategic guidance, and client communication for government mission programs.

Partners with Customer Success leadership to define enablement strategies, build onboarding and development programs, manage roadmaps and cross-functional dependencies, and evaluate effectiveness using data and feedback. Leads learning sessions, equips managers to reinforce skills, and coordinates with Operations, Product, Marketing, and technical enablement teams to prepare customer-facing teams for evolving products, processes, and user needs.

Leads globally distributed software engineering teams responsible for Axon’s core cloud infrastructure platform. Defines technical strategy and architecture across infrastructure as code, compute, networking, observability, reliability, security, and developer experience. Partners with Product, Security, and engineering leaders to deliver scalable systems and customer outcomes. Oversees engineering managers and technical leads, develops talent, drives AI-native engineering practices, and builds a culture of accountability, learning, and high performance.

The Senior Data Scientist develops quantitative models for financial crime compliance, performs data analysis, and collaborates across teams to enhance model performance.

Own VIP customer strategy and operations from planning through measurable execution. Partner with leadership to acquire, engage, and retain high-value customers; run concurrent analytical, strategic, partnership, and technical projects; build AI-enabled models, workflows, and analyses; and collaborate with Data, Product, Engineering, and Marketing in a regulated responsible-gaming environment.

Leads the strategy, architecture, and production scaling of agentic AI capabilities across a Databricks-based data platform on AWS. Oversees AI-enabled data modeling, pipeline generation, classification, governance, tool integration, monitoring, security, and human-in-the-loop controls. Manages cross-functional delivery, technical roadmaps, stakeholder communication, and the hiring and development of AI and data engineering teams. Establishes responsible AI, SDLC automation, resiliency, auditability, and quality standards for enterprise adoption.

Design and build realistic simulated workplace environments, evaluation tasks, benchmarks, graders, and scoring systems for training and assessing AI models. Generate synthetic records, files, and communications; prevent models from exploiting evaluation systems; align benchmarks with real-world performance; and convert user feedback into new test cases. The role is early-career and emphasizes reinforcement learning or model post-training experience.

Leads AI-assisted software development adoption and engineering modernization across SecDevOps teams. Oversees microservices, API integration, data modernization, automation, technical debt reduction, scalability, and cross-team coordination. Develops modernization roadmaps, tracks productivity improvements, identifies delivery risks, and communicates technical progress to executives. Requires strong software architecture, AI development tooling, engineering transformation, technical leadership, and communication experience.

Leads systems engineering strategy across a six-task-order software portfolio. Responsibilities include portfolio rationalization, AI insertion, modernization, DevSecOps adoption, legacy platform transitions, architecture guidance, continuous improvement, sponsor collaboration, and technical oversight of cost, schedule, and performance. The role requires presenting technical strategies to senior government stakeholders and coordinating strategic initiatives across engineering and program teams.

Manage enterprise metadata, business glossaries, data catalogs, and end-to-end data lineage for a federal case management modernization program. Configure governance and metadata platforms, integrate data sources and pipelines, establish standards and workflows, monitor metadata quality, support compliant data sharing, develop governance metrics, document procedures, and train data stewards and business users.

Develops and adapts large language models and NLP methodologies to create intelligence-analysis capabilities. Responsibilities include building, tuning, evaluating, and deploying AI/ML models; preparing data; using deep learning frameworks and GPUs; developing semantic search and text-focused solutions; and communicating modeling results. The role requires strong Python or R, SQL, version control, cloud, and machine learning experience, along with an active TS/SCI clearance with polygraph and onsite work in McLean, Virginia.

Develops and deploys AI solutions for national security applications, including data preprocessing, large language model training, feature engineering, model evaluation, production monitoring, and technical documentation. Collaborates with data scientists, software engineers, product managers, and stakeholders to deliver scalable machine learning solutions. Requires onsite work in Chantilly, Virginia, U.S. citizenship, and an active TS/SCI clearance with polygraph.

Designs and leads enterprise system architectures supporting NASA IT services. Responsibilities include systems integration, cloud and infrastructure design, legacy modernization, technical leadership, architecture documentation, stakeholder engagement, compliance with NASA, NIST, and federal standards, and oversight of scalable, secure solutions. The role requires communication with technical and non-technical stakeholders, mentoring engineering teams, evaluating emerging technologies, and guiding architecture and design reviews.

Develop and maintain BI dashboards and ETL pipelines, transform and integrate structured and unstructured data, build analytical models and forecasts, apply machine learning and distributed computing (Databricks/Spark), create actionable visualizations (Qlik/Tableau/Power BI), and present insights to leadership while working in a cleared defense environment.

Conduct research on agentic AI and data memory systems, including graph-based methods, agent memory, workflows, evaluation, and LLM-based agents. Collaborate with researchers, present findings, contribute to experiments, and potentially publish peer-reviewed work during a 12-week internship.

Performs hands-on cybersecurity and AI engineering, defines and documents functional and technical requirements, converts requirements into Jira stories, validates application features, and collaborates with customers, developers, systems engineers, and AI stakeholders. Evaluates AI architectures, model performance, integration tradeoffs, and emerging AI/ML applications for enterprise software. Requires active TS/SCI with polygraph clearance, U.S. citizenship, a bachelor’s degree, and 15+ years of experience.

Leads enterprise pricing strategy across Federal acquisition and commercial technology offerings. Develops subscription, usage-based, outcome-based, hybrid, and XaaS pricing models; builds financial models, forecasts, investment analyses, and business cases; advises executives on market positioning and monetization; supports strategic pursuits; and partners across finance, contracts, marketing, capture, and solution development to drive profitable, scalable growth.

Collects, validates, and analyzes operational, logistics, readiness, inventory, and supply-chain data. Leads statistical, trend, causal, predictive, and prescriptive analysis; develops dashboards, reports, scorecards, and executive briefings; supports data integration, system testing, process improvement, KPIs, and strategic decision-making. Collaborates with Army, logistics, procurement, and program stakeholders to improve medical materiel visibility, asset accountability, and supply-chain responsiveness.

Own and grow a regional portfolio of major public safety agency accounts in a quota-carrying, full-cycle sales role. Build pipeline, run discovery and demonstrations, manage proof-of-value engagements, navigate procurement and approval processes, and close large multi-year deals. Develop executive relationships across police, fire, emergency management, IT, finance, procurement, and elected leadership. Sell with channel partners, forecast accurately, use AI tools for territory execution, and coordinate internal teams through handoff and expansion.

Drive enterprise sales growth across San Francisco, focusing on AI-native organizations and strategic accounts. Build executive relationships, develop account plans, orchestrate cross-functional sales teams, guide customers through complex DevSecOps transformations, and manage the full sales cycle from discovery through negotiation and implementation. Generate pipeline, expand existing accounts, forecast performance, conduct win/loss analysis, and position GitLab as a long-term technology partner for AI and software engineering organizations.

Supports USCYBERCOM cyberspace operational planning by developing OPLANS, OPORDs, FRAGOs, crisis plans, and concepts of support. Coordinates cyber capabilities, targeting, intelligence integration, operational requirements, and partner activities across DoD and Intelligence Community organizations. Assesses cyber threats, capabilities, tools, and operational performance while supporting planning teams and current operations. Requires active Top Secret clearance with SCI eligibility and polygraph, formal cyber operational planning training, military operations knowledge, and relevant cyberspace planning or capabilities development experience.

Leads enterprise AI/ML engineering teams and defines strategy for AI-ready data and context platforms. Owns semantic architecture, ontologies, knowledge graphs, metadata, data quality, governance, and contextual data products supporting generative AI, intelligent search, decision support, and agentic workflows. Partners with senior stakeholders, translates AI priorities into platform capabilities, evaluates emerging technologies, and mentors technical leaders.

Develop and deploy machine learning models and scalable data pipelines for pharmaceutical sciences and translational safety. Build AI-ready datasets from toxicology, PK/PD, in vivo, biomarker, pathology, and assay data. Collaborate with toxicologists, pharmacologists, safety professionals, and data engineers to support safety signal detection, dose selection, translational research, and scientific decision-making. Apply model governance, data standardization, ontology practices, validation, and cloud-based engineering techniques.

Own and grow defense-sector customer accounts from prospecting through demonstrations, quotes, contracts, and repeat business. Responsibilities include researching target organizations, managing CRM pipelines, forecasting opportunities, tracking funding paths and contract vehicles, supporting demonstrations and exercises, gathering customer feedback, assisting capture and proposal efforts, and building account plans. The role requires collaboration with growth, engineering, contracts, finance, and field operations teams, along with responsible handling of sensitive information.