As a Senior Machine Learning Research Engineer at Symbolica, you will conduct pioneering research on symbolic reasoning using category theory, develop scalable machine learning models, optimize pipelines for structured tasks, and collaborate with researchers to push the boundaries of AI. You will be responsible for translating theoretical insights into practical applications while ensuring the reliability of models in real-world scenarios.

As a GPU Software Engineer at Symbolica, you'll design and optimize GPU kernels and algorithms for symbolic reasoning models. Responsibilities include developing CUDA-based implementations, analyzing performance, managing memory, and collaborating on complex algorithms, emphasizing high-performance computing.

The Machine Learning Infrastructure Engineering Lead will design and optimize infrastructure supporting machine learning research. Responsibilities include managing workflows, developing tools for reproducibility, optimizing resources, and building CI/CD pipelines, while collaborating with scientists and engineers on infrastructure needs.

As a Lead Full Stack Engineer at Symbolica, you'll design, develop, and optimize user-facing and backend systems for AI solutions, build and iterate on prototypes, and collaborate with the research team to create interactive interfaces. This role focuses on full-stack application management, ensuring integration and performance, while enhancing the company's online presence.