Top Artificial Intelligence Software Engineer Jobs

Build and maintain full-stack software using TypeScript, Go, and Python, including backend, frontend, and architectural development. Collaborate with developers, designers, product managers, and Department of Defense stakeholders to develop AI-powered solutions, take products from concept to launch, conduct code reviews, and support customers through travel when needed.

Build and own production-grade APIs, services, and user-facing applications across Python and TypeScript. Architect event-driven systems, deploy serverless workloads, containerize services with Docker on AWS, and establish engineering practices and technical roadmaps. Partner with product and business stakeholders to deliver scalable features, manage projects from concept through deployment, and mentor future engineers. This first engineering hire will have substantial ownership in an early-stage startup and potential to grow into leadership.

Design, develop, and maintain software applications using Node.js, React, and JavaScript. Implement and manage Kafka-based data streaming. Collaborate on problem solving, perform code reviews, and improve development processes and tools.

Own the frontend for a touch-only pharmacy automation machine, from design prototype through production React application. Build operator workflows, interruption and alert models, typed server-owned state, and interfaces driven by WebSocket data. Establish standards for TypeScript, design tokens, testing, accessibility, and code review. Partner closely with product design and on-machine software engineers, then extend the standard to cloud operations and future product screens.

Own developer-experience workflows from discovery through adoption, acting as product manager, designer, and engineer. Build internal tools including TypeScript and React interfaces, backend services, APIs, CLIs, CI/CD integrations, and AI-enabled workflows. Conduct user research, define product scope, design experiences, ship software, measure adoption, and drive rollout. Candidates need 5+ years of software engineering experience and experience building developer-facing products.

Leads engineering strategy, execution, hiring, and team development for enterprise AI software. Owns technical direction across APIs, application services, data pipelines, cloud and ML infrastructure while remaining hands-on with architecture, coding, debugging, and production operations. Partners with product and leadership on roadmap delivery, guides model training and serving decisions, improves reliability and security practices, and develops engineering managers and technical leaders.

Develop and govern enterprise solutions using Microsoft Power Platform, SharePoint Online, Microsoft Purview, and Copilot. Responsibilities include requirements gathering, architecture, application and workflow development, testing, deployment, documentation, migration support, code reviews, and solution lifecycle management. The developer will create custom apps, automations, integrations, and Copilot extensions; support ALM across environments; collaborate with FDIC leadership and users; and mentor junior developers. Occasional nights and weekend migration work may be required with compensatory scheduling.

Support customers with complex database integration issues while contributing directly to Airbyte’s Java and Kotlin open-source connectors. Troubleshoot replication, CDC, query performance, authentication, networking, schema evolution, and warehouse or lake destinations. Reproduce bugs, inspect logs and query plans, improve connector reliability, review community contributions, and collaborate with Engineering, Product, Solutions, Sales, and Customer Success. The role also includes technical documentation, mentoring support engineers, customer onboarding, and developing AI-assisted troubleshooting processes.

Build experimental applications and interactive tools for configuring experiments, monitoring execution, visualizing results, and capturing metadata. Integrate instrument APIs with SDKs, data services, backend systems, and automation workflows. Implement testing, logging, failure recovery, CI, packaging, and deployment improvements. Diagnose issues across application, performance, concurrency, native-code, and hardware layers while collaborating directly with scientists and engineers to deliver reliable software.

Own backend services, APIs, SDKs, and operational tools for connected hardware and data-intensive workflows. Design reliable data models and asynchronous systems that handle retries, duplicate events, interrupted transfers, and device disconnections. Build telemetry and diagnostics, maintain production reliability and data integrity, test workflows end to end, and support authentication, versioning, documentation, monitoring, and backward compatibility.