Top Tech Jobs & Startup Jobs in Charlotte, NC

Design, build, and maintain scalable big data pipelines, architectures, distributed databases, and storage systems. Use Python and frameworks such as Hadoop, Spark, or Flink for data integration, transformation, and processing. Ensure system performance, availability, security, governance, and data quality while collaborating with cross-functional teams and documenting technical solutions.

Complete, review, and document realistic Airtable workflows for AI training. Create and manage bases, tables, views, fields, records, relationships, automations, forms, interfaces, and integrations. Demonstrate Airtable use across project, product, and business operations workflows; evaluate the realism and accuracy of instructions; and identify efficient information-management approaches.

Evaluate and improve AI-generated outputs for accuracy, clarity, quality, and business alignment. Develop and refine prompts, annotate data, conduct rubric-based assessments, fact-check content, and provide actionable feedback on model performance. Create and edit technical documentation, strategy reports, market analyses, business cases, executive summaries, and professional communications. The role requires strong analytical reasoning, writing, research, quality assurance, and attention to detail.

Create reinforcement learning environments to evaluate AI systems solving complex software engineering tasks with MCP tools. Responsibilities include designing reproducible environments, deterministic verification, golden reference solutions, bug fixes, feature implementation, codebase refactoring, and performance optimization across software codebases.

Part-time remote contractor who evaluates and improves AI-generated software engineering content. Responsibilities include prompt authoring, rubric-based model evaluation, technical documentation, fact-checking, data annotation, research, and detailed feedback on AI outputs. The role requires strong software engineering knowledge, technical writing, analytical reasoning, and attention to detail. Three or more years of professional software engineering experience is preferred, along with experience reviewing technical artifacts such as architecture documents, RFCs, design specifications, or code reviews.

Identify companies with valuable operational data for AI training and evaluation, conduct outbound prospecting, run discovery calls, explain data partnership value and technical concepts, address privacy and ownership concerns, negotiate terms, close B2B deals, manage the sales pipeline, and coordinate partner onboarding with internal teams.

Build reinforcement learning environments to evaluate AI systems on software engineering tasks involving MCP tools. Responsibilities include creating reproducible environments, deterministic verification, golden reference solutions, bug fixes, feature development, codebase refactoring, and performance optimization. The role is remote, flexible, contract-based, and output-focused, with no prior AI experience required.