About the role: ML developers today face significant friction when deploying trained models. They work in a fragmented space with incomplete, patchwork solutions that require extensive performance tuning and model-specific optimizations. At Modular, we are building the
About the role: Modular is building a next-generation AI infrastructure platform that unifies the many application frameworks and hardware backends, simplifying deployment for AI production teams and accelerating innovation for AI researchers and hardware developers. We are
About the role As a Driver Engineer, youll work on the layer that sits between Modulars compiler and runtime stack and the silicon — the devices, contexts, queues, synchronization primitives, memory allocators, networking, and collectives that every
About the Role: ML developers today face significant friction in taking trained models into deployment. They work in a highly fragmented space, with incomplete and patchwork solutions that require significant performance tuning and non-generalizable, model-specific enhancements.
About the Role: Mojo is our secret sauce, spell-book, and Mjolnir, a new programming language that blends the soul of Python with the raw, elemental power of C. It’s designed to unlock every ounce of performance
About the role: ML developers today face significant friction when deploying trained models. They work in a fragmented space with incomplete, patchwork solutions that require extensive performance tuning and model-specific optimizations. At Modular, we are building the
About the role: Mojo is a new systems programming language, designed from scratch for the AI era. Mojo language, compiler and SDK are being rapidly built by a team of talented engineers here at Modular. The Mojo
About the role: This is a technical leadership (IC) role in collaboration with the compiler engineering manager; you will be driving the technical roadmap for the Mojo compiler backend, creating new features, proposing new ideas, driving
About the role: ML developers today face significant friction in taking trained models into deployment. They work in a highly fragmented space, with incomplete and patchwork solutions that require significant performance tuning and non-generalizable, model-specific enhancements.
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