Top Tech Jobs & Startup Jobs in Chicago, IL

Develop and evaluate analytic cases for AI systems across scaled-analysis and pipeline-integration scenarios. Map use cases to taxonomies, assess model responses, identify over-refusal and failure modes, document edge cases, and provide actionable feedback. The role requires expertise in data science, analytics, LLM integration, technical documentation, and evaluation methodology, with government, defense, or intelligence experience valued.

Build and scale distributed data pipelines and architectures supporting AI products and research. Responsibilities include processing large datasets with Spark, designing AWS data systems, managing SQL and NoSQL storage, writing Python and SQL, ensuring data quality and reliability, and implementing automation, orchestration, monitoring, and scalable data operations.

Analyze, debug, and secure diverse codebases; implement features; refactor legacy software; conduct audits and penetration tests; optimize backend performance; and document findings to improve AI training datasets.

Reviews and analyzes large business datasets for accuracy, completeness, relevance, privacy, and quality. Conducts document reviews involving PII, identifies data issues and anomalies, provides feedback to support AI training, refines quality assurance standards, and communicates findings to project stakeholders while maintaining data privacy and security compliance.

Annotate, label, review, and validate audio and video samples according to project guidelines. Identify and correct errors, document annotation decisions, resolve questions with project teams, meet accuracy and deadline requirements, and provide feedback to improve annotation workflows. Strong English communication, attention to detail, organization, and quality control are essential; familiarity with data-labeling tools is advantageous.

Review and annotate robotic-system video footage by identifying objects, movements, events, visual features, and anomalies. Provide feedback to improve video datasets and annotation consistency, collaborate with AI teams on annotation protocols, document observations, participate in quality reviews, and recommend improvements to video capture and labeling strategies.

Provides empathetic, efficient customer support across multiple channels; documents interactions in CRM software; analyzes issues and proposes solutions; applies product knowledge to educate users; shares customer feedback and sentiment trends; develops support documentation; and supplies scenario-based input to train AI systems. Requires at least three years of professional customer support experience, strong communication and problem-solving skills, CRM proficiency, and experience managing complex cases.

Apply cyber threat intelligence expertise to train and evaluate AI systems. Map CTI cases to surveillance and privacy taxonomies, author boundary cases involving prohibited surveillance and intrusion requests, grade threat responses, detect over-refusal, document nation-state and advanced-threat edge cases, and provide clear feedback. The role requires expertise in nation-state operations, attribution, APT tracking, defensive analysis, and distinguishing permissible defensive work from offensive or surveillance-focused activity.

Design, tune, model, validate, and deploy classical and advanced control systems on physical hardware such as robotics, drones, automotive, and industrial systems. Develop plant models, implement control algorithms in Python and open-source tools, analyze performance, document decisions, and collaborate remotely with interdisciplinary teams.

Analyzes computer systems and business processes, gathers and validates stakeholder requirements, models workflows using BPMN or UML, and uses SQL for data analysis and troubleshooting. The role supports process optimization, technical issue resolution, virtual collaboration, and validation of AI training data and scenarios for customer projects.