Top Artificial Intelligence Software Engineer Jobs

Develop and lead full-stack software development for AI-powered defense and national security products. Responsibilities include architecting scalable backend systems, building React front-end components and Node.js services, designing APIs and databases, conducting code reviews, defining software development standards, collaborating with product and UX/UI teams, gathering customer feedback, training users, and implementing automated testing and CI/CD practices.

Build and own Roboflow Labs’ data platform, including catalog, delivery, contributor payout, and shared data infrastructure. Develop reliable pipelines for dataset ingestion, validation, packaging, provenance, rights tracking, and auditability. Automate quality controls such as consensus review, fraud detection, duplicate detection, and quality metrics. Establish engineering standards for architecture, observability, code review, releases, and on-call practices while partnering with operators and integrating with Roboflow’s core platform.

Build and operate scalable backend infrastructure for financial data, including ingestion, transformation, integration, and serving systems. Design high-performance distributed pipelines handling millions of documents and datapoints, support AI and retrieval systems, and integrate heterogeneous financial sources. Ensure secure, compliant, reliable handling of customer data while collaborating with globally distributed engineering teams and influencing technical direction.

Lead the Desktop Software Engineering team, ensuring application reliability, managing desktop systems, and building effective endpoint engineering processes.

Build innovative software products in the Special Projects Division addressing public health, agriculture, education, and aid-related challenges in Sub-Saharan Africa. Responsibilities include designing software architectures, developing on UNIX-based platforms, participating in code reviews, using disciplined version control, collaborating with teams, and potentially applying Django, React, Android, or PyTorch. The role may involve travel to Africa to understand local contexts.

Build and own full-stack financial wellness features using Rails, Python, TypeScript, and React. Design data models and architecture, manage production services, monitoring, on-call, and incident response, and address technical debt and application security. Collaborate with advisers, product teams, clients, and executives to translate workflows into software. Evaluate AI tools responsibly, protect sensitive financial data, mentor engineers, and lead cross-team initiatives.

Lead development of Chariot’s claims processing pipeline and CRM systems, transforming manual scripts into reliable, observable production infrastructure. Build claimant and law-firm workflows, fraud detection and verification stages, dashboards, integrations, internal tooling, and AI agent harnesses. Own product decisions, recruit and develop engineers, and establish pipeline reliability. The role requires staff-level software ownership, early-stage startup experience, and proficiency with Go and AWS.

Build and maintain payment connectors and web/mobile applications, collaborate with designers and developers, implement new features, troubleshoot and optimize existing systems, adopt new technologies, and deliver high-quality solutions within an Agile team focused on ecommerce and payments integrations.

Build internal software for field collection operations, including tasking, site setup, device inventory, shift tracking, local tools, and site-health monitoring. Improve operational efficiency by observing field workflows, removing bottlenecks, and making site expansion repeatable. Integrate field tools with data infrastructure and marketplace incentives, while collaborating closely with on-the-ground teams and occasionally traveling to field sites.

Entry-level engineering trainees will contribute across the software and data development lifecycle, including requirements analysis, architecture, coding, testing, deployment, documentation, and maintenance. They will gain hands-on experience with cloud, data engineering, LLMs, prompt engineering, autonomous AI agents, ETL/ELT pipelines, distributed data systems, APIs, CI/CD, and software quality practices. The role requires collaboration, problem-solving, communication, adaptability, and full-time work from the Schaumburg office.