Top Tech Jobs & Startup Jobs in Chicago, IL

Create challenging coding tasks for AI systems, implement deterministic solution verifiers, and contribute backend expertise across feature development, bug fixing, refactoring, performance optimization, testing, and security reviews. The role requires strong programming and software engineering skills, with cybersecurity experience in secure coding, application security, penetration testing, vulnerability assessment, or security-focused code reviews preferred.

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.

Designs vulnerable and secure application fixtures addressing OWASP LLM risks, creates deterministic tests and security manifests, develops answer keys for AI and scanner evaluation, and improves AppSec assessment pipelines. The role also evaluates traditional and agent-based security architectures, documents technical findings, and collaborates on peer reviews and actionable security recommendations.

Build and secure scalable backend services and APIs while debugging, optimizing performance, implementing features, refactoring codebases, and conducting security reviews and penetration testing. The role also involves automated testing, code review, vulnerability assessment, and contributing domain expertise to training next-generation AI systems.

Develop and maintain scalable Java backend systems using Spring Boot and microservices architecture. Collaborate with customer teams, review specifications, write clean code, participate in code reviews, document solutions, and troubleshoot and optimize backend performance, reliability, and security. The role also contributes technical expertise to training next-generation AI systems, though prior AI experience is not required.

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.

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.

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.

Conduct aerodynamic and fluid-dynamics analyses for an AI training project. Build geometries, computational domains, meshes, boundary conditions, and solver-ready CFD cases; run command-line simulations; automate workflows with Python; perform parameter sweeps and postprocessing; and validate numerical and physical results. Diagnose convergence issues and communicate modeling assumptions and engineering trade-offs. Requires an aerospace or mechanical engineering background with aerodynamics expertise and practical CFD and Python experience.