Top AI & Machine Learning Jobs

Designs, configures, deploys, and optimizes Salesforce Agentforce AI agents integrated with Data Cloud. Develops Apex, Lightning Web Components, Flows, prompt templates, AI model integrations, and RAG solutions. Troubleshoots Salesforce and Agentforce issues, monitors AI performance, documents solutions, collaborates with stakeholders, and mentors junior developers.

Lead AI transformation across Sales, Marketing, Customer Success, Partnerships, and Revenue Operations. Identify high-value opportunities, design and deploy production AI agents, agentic workflows, automations, integrations, and enterprise solutions, then drive adoption and measure business impact. Partner with functional leaders, make sound decisions about AI suitability, redesign operating models, improve reliability and governance, and mentor colleagues while establishing reusable standards across the organization.

Perform data science, engineering, analytics, and statistical work for Department of War and Intelligence Community clients. Test and evaluate AI/ML models, collaborate with teams, communicate findings, and support or conduct all-source analysis. Experience with GEOINT and large language models is preferred. The role requires an active TS/SCI clearance and ability to pass a CI polygraph.

Own the architecture, roadmap, and production operation of Podium’s AI agent platform. Lead engineers building orchestration, tool use, memory, reasoning, evaluation, safety, and reliability systems for customer-facing agents. Drive capabilities from prototype to scale, optimize latency, cost, and quality, and partner with Product, Infrastructure, and customer teams. Establish engineering practices for testing and responsible deployment while representing AI Agents engineering in leadership decisions.

Supports enterprise AI transformation through machine learning, generative AI, data science, retrieval-augmented generation, and workflow automation. Responsibilities include data preparation, feature engineering, model development and evaluation, monitoring, dashboard and documentation creation, and secure AI application development. The intern collaborates with IT, AI transformation teams, data stewards, and business stakeholders to deliver practical AI and analytics solutions while following security, privacy, and responsible AI requirements.

Leads enterprise-wide AI strategy, governance, use-case development, vendor evaluation, pilot programs, implementation, change management, risk mitigation, and executive reporting. Partners with leaders across construction, manufacturing, service, VDC, estimating, business development, and finance to identify and scale high-value AI and automation initiatives while measuring ROI and adoption.

Develops production-ready AI, data automation, integration, and workflow solutions for engineering processes in an aerospace and defense environment. Responsibilities include programming, API and database integration, engineering toolchain integration, CI/CD, testing, documentation, workflow analysis, roadmap development, training, and user support. The role collaborates with systems engineers and customer teams and may deploy tools in classified environments.

Leads the design, development, integration, verification, and flight-test delivery of machine learning and autonomy capabilities for uncrewed aircraft. Responsibilities include perception, tracking, sensor fusion, planning, mission execution, model deployment, real-time C++ and Python software, simulation, testing, troubleshooting, and cross-functional system integration. Provides technical leadership and mentorship without direct personnel management. Requires on-site aircraft integration and field-test support in Bingen, Washington.

Conduct research at the intersection of mechanistic interpretability and reinforcement learning. Develop methods to analyze language-model and agent internals, derive intrinsic rewards and training signals, run experiments, evaluate failure modes, and build reproducible research infrastructure. The fellow will own a focused project, collaborate with technical staff, communicate findings, and contribute to publications.

Develop and implement statistical and machine learning trading strategies and quantitative models. Design optimization algorithms for portfolio construction and explore new research opportunities within derivatives and market behavior.