Top AI & Machine Learning Jobs

Lead client-facing AI program governance and delivery as PMO manager. Translate business objectives into AI roadmaps and OKRs, oversee AI/ML lifecycle, ensure data readiness, model validation, deployment, and responsible AI compliance. Manage PMO standards, portfolio prioritization, AI-augmented reporting, and lead executive communications while mentoring PMs, TPMs, and analysts.

Build customer-facing data products for commercial vehicle customers, including forecasting, pricing signals, recommendations, matching, ranking, enrichment, and market intelligence. Apply LLMs and machine learning to messy structured and unstructured data, while creating evaluation, monitoring, and guardrail systems. Design ingestion, transformation, feature, entity-resolution, and data-quality pipelines. Partner with product, design, customers, and commercial teams, define success metrics, ship reliable solutions, and mentor analysts and engineers.

Assistant Professor conducting research and teaching on AI, machine learning, data analytics, and their political, regulatory, ethical, and socioeconomic implications. Responsibilities include mentoring undergraduate and graduate students, engaging in interdisciplinary collaborations, pursuing external grant funding, and contributing to Ohio State’s socio-political analysis and AI initiative.

Build and orchestrate AI automation workflows using n8n, LLMs, agentic frameworks, and MLflow. Develop intelligent agents, chatbots, semantic retrieval systems, and AI dashboards with Streamlit, OpenWebUI, Pinecone, OpenAI, and Anthropic technologies. Integrate AI into business processes and data platforms, extract structured document data, and ensure compliance, security, and ethical standards. The role is primarily office-based in Charlotte, NC, with occasional travel.

The AI Systems Engineer will build production systems for AI agents handling customer interactions, optimize omnichannel experiences, and integrate with CRM systems.

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.

Build and own AI team systems spanning data ingestion, labeling, training and evaluation infrastructure, model deployment, perception integration, and customer-facing applications and APIs. Drive high-impact technical projects, make hands-on architectural decisions, mentor engineers, and support a broad AI software stack. The role is approximately 70% backend and 30% frontend, with focus areas including ML Ops, data pipelines, or computer vision.