Top Tech Jobs & Startup Jobs in Charlotte, NC

Lead AI training project development based on real-world mechanical engineering workflows. Design multidisciplinary engineering scenarios, define requirements and acceptance criteria, decompose complex problems into technical tasks, coordinate engineering contributions, and evaluate design feasibility. Apply expertise in mechanical design, analysis, manufacturing, verification, testing, and root-cause analysis to ensure realistic professional standards and deliverables.

Investigates insurance claims for staged losses and fraud by analyzing CCTV, dashcam footage, photographs, statements, engineering reports, and other evidence. Reconstructs incidents, evaluates authenticity and causation, identifies inconsistencies and fraud patterns, and produces objective written assessments for insurance and SIU professionals. Contributes investigative expertise to AI training and evaluation datasets while collaborating remotely with project stakeholders.

Analyze consumer product safety laws, regulations, directives, and standards across jurisdictions. Review and evaluate legal and compliance documentation, assess product safety scenarios and compliance strategies, identify regulatory gaps and risks, and provide practical guidance. Validate AI-generated legal content for accuracy and completeness, contribute structured legal analysis to AI training datasets, and collaborate with stakeholders to deliver high-quality work.

Evaluates and compares chatbot responses against business requirements and quality criteria. Gathers requirements, documents functional specifications, analyzes workflows and data, creates reports and dashboards, supports testing and UAT, tracks project risks and KPIs, and presents evidence-based findings. Collaborates with technical teams and stakeholders to improve AI output quality while maintaining evaluation consistency and meeting deadlines.

Design, implement, test, debug, and evaluate production-grade Rust backend systems for AI training projects. Responsibilities include API and persistence development, asynchronous and concurrent processing, performance optimization, reliability analysis, architecture review, reproducible test environments, containerized workflows, and technical documentation. The role requires identifying correctness, scalability, and failure-handling issues while delivering high-quality implementation artifacts and meeting weekly project deadlines.

Develop and validate machine learning models, training pipelines, inference systems, and numerical methods using Python. Debug algorithmic and system-level issues, optimize memory usage and performance, design systematic tests, and build reproducible workflows. Review challenging ML engineering tasks, use coding agents in development, document technical findings, and deliver high-quality work independently within deadlines.