Top Artificial Intelligence Software Engineer Jobs

Build and refine customer-facing React features and internal dashboards, implement TypeScript-based UIs in strict mode, integrate frontend with backend APIs, and contribute to product and design decisions for an AI-driven scheduling assistant.

Lead architecture and technical strategy for a fullstack digital pathology platform. Design and deliver complex cross-team initiatives, write production code, uphold engineering standards, and improve testing, observability, security, and performance. Collaborate with product, design, machine learning, infrastructure, QA, and regulatory teams in a medical device environment. Mentor engineers, influence technical decisions, and communicate with customers and nontechnical stakeholders.

Build and maintain scalable integrations connecting HRIS, ATS, payroll, CRM, and ERP systems. Develop APIs, connectors, data pipelines, and Temporal workflows while handling authentication, pagination, rate limits, schema changes, retries, and failures. Optimize data ingestion, improve customer-facing error reporting, and collaborate with Customer Success, Implementation, and engineering teams. The role also involves documentation, integration architecture, security practices, and occasional frontend work.

Build and own end-to-end product experiences across APIs, SDKs, and web interfaces, including billing, authentication, usage dashboards, and cost reporting. Integrate billing systems and vendor APIs, develop observable and auditable systems, and collaborate with finance, product, design, and engineering teams. The role requires API design, React development, cloud experience, automation, and AI-assisted coding practices.

Design and scale backend services, data pipelines, event-driven systems, and integrations supporting autonomous manufacturing operations. Build LLM agents and RAG workflows for supplier document analysis, manage PostgreSQL data models, integrate ERP and freight APIs, implement caching and rate limiting, and establish observability and incident response practices. The role requires transactional processing expertise, NLP/ML pipeline experience, and manufacturing supply chain domain knowledge.

Build and own user-facing features and core platform architecture across frontend, backend, and ML pipelines. Integrate AI workflows (LLMs, embeddings) for curriculum generation and automated evaluation. Manage end-to-end development: requirements, design, implementation, CI/CD, testing, observability, and iteration while collaborating with product, design, ML, and customer teams.

Build and own end-to-end products across Paraform’s recruiting marketplace, AI matching systems, and recruiting operating system. Responsibilities include identifying high-impact problems, designing solutions, shipping across the stack, improving candidate and recruiter experiences, developing agentic recruiting systems, and creating CRM, workflow, and integration tools. The role requires strong product and business judgment, technical versatility, autonomy, and ownership of outcomes.

Build and deploy AI workflows, integrations, plugins, data pipelines, and proofs of concept for major customers. Diagnose production blockers, run technical evaluations and managed trials, scope deployments, accelerate customer onboarding, and convert successful solutions into reusable products. Partner with executives, engineers, Product, Engineering, and Creative teams while measuring customer outcomes and feeding recurring needs into the product roadmap.

Develop, maintain, and enhance full-stack Java applications for a federal agency. Responsibilities include building REST APIs and microservices, writing automated tests, troubleshooting production issues, optimizing SQL and NoSQL database use, conducting code reviews, and contributing to architecture decisions. The developer will collaborate with product managers, QA engineers, and other developers while following federal standards, secure development practices, and DevSecOps processes.

Build across the full stack to create and iterate on AI agent products. Conduct user interviews, prototype features, ship frontend and backend code, collaborate with design, and help define product roadmap and direction for a 0-1 product.