Top AI & Machine Learning Jobs

Build and operate AI agents, GTM infrastructure, account-data systems, paid and ABM automation, CRM integrations, sending infrastructure, and deliverability programs. Own Salesforce, routing logic, integrations, data quality, observability, permissions, and auditability. The role is accountable for pipeline, revenue, reply rates, agent reliability, and cost efficiency. Candidates should have shipped production software, run scalable outbound or paid programs, applied AI to GTM workflows, and worked with Python or TypeScript, SQL, APIs, and Salesforce.

Build and deploy production-grade generative AI and autonomous agent applications. Responsibilities include designing agent orchestration and RAG systems, developing secure enterprise connectors, deploying cloud applications, creating evaluation and observability frameworks, and improving reliability, scalability, latency, and cost. The role translates business needs into technical roadmaps, applies AI governance and security practices, collaborates with non-technical stakeholders, and develops reusable architecture and components in a regulated energy environment.

Develops machine learning models, statistical analyses, forecasts, and Power BI reporting to support sales, pricing, marketing, finance, demand planning, and churn analysis. Builds SQL data models, reconciles commercial data, automates workflows, manages analytics projects, gathers requirements, and advises business users on data and reporting best practices. The role also promotes data quality, process improvement, and regulatory compliance.

Develop and lead software and computer vision algorithms for semiconductor inspection platforms. Responsibilities include system-level problem decomposition across optics, mechanics, electronics, and software; image processing, machine learning, calibration, segmentation, classification, and detection; software design, coding, testing, optimization, and debugging; cross-functional product development; architecture ownership, documentation, mentorship, and customer problem resolution. International travel to customer sites of at least 20% is required.

Develop and integrate AI/ML capabilities for classified Battlespace C2, threat intelligence, data extraction, modeling, dashboards, and threat inject generation. Configure virtualized test environments, execute cybersecurity scans, maintain secure baselines, support RMF and ATO compliance, and perform integration and regression testing. Create technical documentation, track defects and configuration changes, and collaborate with software, systems, cybersecurity, and government stakeholders. The role requires Secret clearance with Top Secret/SCI eligibility and 5% travel.

Tenure-track Assistant Professor researching and teaching at the intersection of artificial intelligence and public policy. Responsibilities include developing an externally funded research program, teaching undergraduate and graduate courses in AI, data literacy, computational methods, and technology policy, contributing to interdisciplinary AIM initiatives, and collaborating across the university on AI governance, regulation, security, economic, social, and environmental policy issues.

Conduct research advancing assurance methods for embodied AI systems, including robots, drones, and autonomous vehicles. Develop prototypes to evaluate safety and reliability methods, define research directions, collaborate with universities, publish scientific findings, and present work at conferences. The role requires machine learning model development, digital tool implementation, and clear communication of complex concepts.

Leads the strategy, roadmap, evolution, and enterprise scaling of AI/ML platforms, GenAI tooling, agent frameworks, and shared infrastructure. Partners with engineering, security, architecture, product, and business teams to deliver secure, reliable, governed, and cost-effective AI capabilities. Establishes platform standards, guardrails, success metrics, and responsible AI practices while managing vendors and accelerating production AI adoption across the organization.

Leads the architecture, development, security, and operation of the company’s AI platform, data foundation, internal applications, and automations. Responsibilities include building distributed systems, cloud data platforms, semantic layers, knowledge graphs, LLM applications, agentic workflows, MCP integrations, and governed automation. The role establishes engineering standards, leads hiring and mentoring, manages AI governance, and owns production systems end to end, including infrastructure, access controls, monitoring, cost, performance, and compliance for sensitive data.

Build and publish AI-driven computer vision systems for real-time sports video analysis, large-scale game analytics, and fan-facing features. Collaborate with CMU researchers, take prototypes to production, and mentor students while deploying league-wide analytics platforms.