Top Artificial Intelligence Software Engineer Jobs

Build polished, high-performance React/TypeScript web applications and end-to-end features using FastAPI and Postgres. Lead frontend architecture, state management, and UX for data-dense scientific workflows. Collaborate with scientists, robotics, and AI teams, leverage LLMs to accelerate prototyping, ensure testable, secure code, and mentor junior engineers while shipping production-ready tooling for laboratory automation and imagery workflows.

The Head of Application Development will lead the development team, establish best practices, and implement innovative technologies to align with business needs.

The Frontend Systems Architect will lead frontend architecture, optimize performance, mentor engineers, and drive product technical direction while ensuring quality and stability in large-scale web applications.

As a Forward Deployed Engineer, you will lead technical discussions, design prototypes, and simplify complex solutions for AI applications. You'll empower customers and provide feedback to enhance product development, while actively engaging in the evolving AI landscape.

The Technical Lead will own technical success, drive architecture decisions, mentor engineers, ensure quality, and engage stakeholders for software delivery projects.

As an ML Infrastructure Engineer, you'll develop platforms and solutions for running ML jobs efficiently, establish CI/CD practices, and lead initiatives in technology alignment.

Develop data pipelines and AI systems to process data from multiple sources. Focus on simplicity, reliability, and maximizing sales efficiency while shipping features regularly.

The intern will develop features for the AI-powered video advertising platform, collaborating with engineers and PMs on full-stack solutions.

As a Senior Data Engineer, you will design and develop AI-powered data solutions, investigate tools, and collaborate with engineering to enhance Sleuth's platform.

The role involves developing software for optical test automation, managing data pipelines, collaborating across teams, and enhancing testing efficiency for silicon photonics.