Top Artificial Intelligence Jobs in Pittsburgh, PA

Manage GTM operations, build reports and dashboards, analyze performance data, coach SDRs, and develop prospecting strategies.

The Content & Prompt Operations Specialist manages AI-assisted content workflows, ensuring accuracy and engagement in content produced. Responsibilities include writing prompts, drafting content, proofreading, and improving AI outputs while following structured processes.

The Product Manager will lead product direction and execution for client projects, focusing on defining requirements, managing backlogs, and collaborating with teams to deliver AI-enabled solutions, while also mentoring Ascendance Fellows.

Lead cross-EHR integration strategy and workflow design, advise Product and Engineering on EHR APIs and feasibility, align cross-functional teams, reduce integration risk, and accelerate multi-EHR time-to-market.

Lead and scale the customer success organization, managing customer lifecycle, driving ARR expansion, and fostering a data-driven culture.

Lead strategic partnerships and business development to drive revenue in cybersecurity. Develop partnerships, negotiate deals, and collaborate cross-functionally for growth.

Design, build, and deploy end-to-end production ML systems for audience targeting, lookalike generation, and propensity scoring. Own ML lifecycle from experimentation to production monitoring, leverage distributed/cloud platforms, implement vector similarity and embedding systems, engineer scalable features, and collaborate with engineering and product teams to ensure model quality and operational reliability.

The Customer Success Manager will manage strategic accounts, drive customer adoption, identify growth opportunities, and ensure customer satisfaction and retention through proactive engagement and collaboration.

The Research Intern will assist in data curation, exploratory analysis, and implementation of machine learning models in healthcare applications.

The Data & CVML Manager leads the development of autonomy for vehicles, focusing on data and ML processes from collection to deployment, while managing a team and ensuring technical performance aligned with safety requirements.