Top Artificial Intelligence Software Engineer Jobs

Build and ship full‑stack product features using Next.js and TypeScript, design database schemas and queries, implement Temporal workflows, develop responsive accessible UI components, iterate from customer feedback, and influence architecture and tooling decisions.

As a Software Engineering Intern at Caddi, you'll enhance AI-driven automation systems through backend development, workflow evaluation, and collaboration with engineers.

Join BRM as an Entry Level Software Engineer, collaborating on AI-driven projects, learning modern technologies, and contributing to software solutions.

Lead and scale the iOS mobile platform end-to-end: own architecture, roadmap, delivery, and reliability. Drive major performance and crash-rate improvements, build observability and automated testing, coordinate closely with Product/Design/Backend, and improve EHR integrations and technical debt while establishing mobile engineering processes and execution cadence.

Rapidly prototype and iterate AI-powered features (LLMs, embeddings, real-time audio, APIs), lead experiments from hypothesis to proof-of-concept, collaborate with product and engineering to translate prototypes into product-aligned solutions, and share learnings and artifacts to accelerate team innovation.

As a Staff Full Stack Engineer, you'll lead architecture, resolve technical challenges, and ensure system reliability while mentoring engineers. This role combines technical design, coding, and cross-team influence to deliver AI-powered software in safety-critical environments.

As a Software Engineer - New Grad, you will build and ship production features across the stack, design scalable systems, and learn in a fast-paced environment.

The role involves designing and building AI-focused research tools, optimizing performance, engaging with customers, and scaling product features quickly.

Design and implement low-latency teleoperation and XR systems across the stack: build Unreal Engine operator apps, optimized data streaming over unreliable networks, device frontends (Android/embedded), integrate hardware SDKs, write shaders, and scale infrastructure with observability and CI automation.

Develop and optimize GPU algorithms and libraries to improve AI model inference, collaborating directly with the technical team, and investigating new architectures.