Are you excited about re-engineering AI from first principles? Join as a founding engineer and scientist! We value demonstrated aptitude and passion, whether through publications, blog posts, or GitHub projects. If you're eager to push boundaries and solve the AI energy crisis, we want to hear from you!
This is a research position aimed at improving our ML training algorithms with a focus on making training faster, more stable and to scale our methods to larger models. Think: "How can we make this model architecture work".
Useful skills: You are well versed with the deep learning literature, can jump between theory (e.g. calculating gradients) and code (e.g. PyTorch), and have worked with esoteric/non-differentiable systems (e.g. binary neural networks, spiking neural networks, sparse neural networks). Big bonus if you can optimize GPU kernels.
We are looking for an engineer to bridge cutting-edge technology with real-world applications. In this role, you will help translate our innovations into customer-facing solutions by rebuilding existing ML solutions using e.g. object detection, GenAI, LLMs etc. Think: "How can we use this model architecture to solve this real life domain problem".
Useful skills: Broad knowledge of ML literature and deep knowledge of specific domain (GenAI, LLMs).
We are seeking a low-level systems engineer who thrives on the challenge of squeezing every bit of performance out of hardware. If you're passionate about optimizing CPU and GPU performance and enjoy tackling problems like running many bit operations efficiently, this role is for you.
Useful skills: You know the in-and-outs of CPUs and GPUs (e.g. SIMD, caching, read/write latencies, memory hierarchies) and how to max-out performance for executing many bit operations in parallel. Big bonus if you know compiler design and FPGAs (Verilog/VHDL).