Top Artificial Intelligence Software Engineer Jobs

Build and scale ReadyOn’s real-time backend decision layer. Design and implement decoupled TypeScript/Node.js (NestJS) services, work with Python components, optimize PostgreSQL schemas and queries, manage migrations and concurrency, integrate GraphQL, and collaborate with product, design, and AI teams to deliver high-impact production features. Onsite in San Francisco.

Design and implement full stack solutions, build APIs, create data visualization tools, and develop AI/ML solutions for the defense sector.

The Full Stack Software Engineer will design and maintain backend microservices, frontend applications, and C++ edge processing systems, ensuring scalability and reliability.

Lead development of Hooks Android app from 0->1: design architecture, build native, performant media-rich features, integrate backend APIs, ensure UX quality, set Android best practices, and monitor performance and stability.

Own and build the data platform: design real-time ingestion, schemas, and pipelines for high-volume ML jobs; consolidate and cleanse messy datasets; enable agent context and developer access to analytics.

Lead end-to-end development of a highly interactive, AI-driven-feeling single-page web app with a focus on exceptional UI/UX, performance optimization, and product ownership. Collaborate with customers, product, design, and engineering; ship features quickly; participate in code reviews, interviews, and culture-building.

Lead product growth by combining frontend engineering, product, and marketing. Analyze product usage with analytics dashboards, run top-of-funnel and engagement experiments, define metrics, and optimize revenue and pipeline pre- and post-sale.

Build and maintain API services for a decentralized exchange, design and implement trade routing logic for optimized cross-chain swaps, integrate features with cross-functional teams, monitor system performance and reliability, and troubleshoot trade execution and API issues.

Own and scale core data systems powering high-throughput ingestion, storage, analytics, and product surfaces. Stabilize write paths, design read-optimized models and partitioning strategies, separate transactional and analytical workloads, enable AI-native analytics, write RFCs, and raise reliability and performance standards across the team.

Own and operate the end-to-end creation platform (frontend, backend, infra, models). Improve real-time generation latency and reliability, optimize cost at scale, maintain queues/fallbacks, and ship continuous improvements to the core create/remix loop and feed/ranking systems.