Job description

About Etched Etched is building AI chips that are hard-coded for individual model architectures. Our first product (Sohu) only supports transformers, but has an order of magnitude more throughput and lower latency than a B200. With Etched ASICs, you can build products that would be impossible with GPUs, like real-time video generation models and extremely deep & parallel chain-of-thought reasoning agents. Etched Labs is the organization within Etched whose mission is to democratize generative AI, pushing the boundaries of what will be possible in a post-Sohu world. Key responsibilities Propose and conduct novel research to achieve results on Sohu that are unviable on GPUs Translate core mathematical operations from the most popular Transformer-based models into maximally performant instruction sequences for Sohu Develop deep architectural knowledge informing best-in-the-world software performance on Sohu HW, collaborating with HW architects and designers. Co-design and finetune emerging model architectures for highest efficiency on Sohu Guide and contribute to the Sohu software stack, performance characterization tools, and runtime abstractions by implementing frontier models using Python and Rust. Representative projects Propose and implement a novel test time compute algorithm that leverages Sohu’s unique capabilities to unlock a product could never be achieved on a typical GPU Implement diffusion models on Sohu to achieve GPU-impossible latencies that allow for real-time image generation Optimize model instructions and scheduling algorithms to optimize for utilization, latency, throughput, and/or a mix of these metrics. Implement model-specific inference-time acceleration techniques such as speculative decoding, tree search, KV cache sharing, priority scheduling, etc by interacting with the rest of the inference serving stack. You may be a good fit if you have An ML Research background with interests in HW co-design Experience with Python, Pytorch, and / or JAX Familiarity with transformer model architectures and/or inference serving stacks (vLLM, SGLang, etc.) and/or experience working in distributed inference/training environments Experience working cross-functionally in diverse software and hardware organizations Strong candidates may also have ML Systems Research and HW Co-design backgrounds Published inference-time compute research and/or efficient ML research Experience with Rust Familiarity with GPU kernels, the CUDA compilation stack and related tools, or other hardware accelerators Benefits Full medical, dental, and vision packages, with 100% of premium covered Housing subsidy of $2,000/month for those living within walking distance of the office Daily lunch and dinner in our office Relocation support for those moving to Cupertino How we’re different Etched believes in the Bitter Lesson . We think most of the progress in the AI field has come from using more FLOPs to train and run models, and the best way to get more FLOPs is to build model-specific hardware. Larger and larger training runs encourage companies to consolidate around fewer model architectures, which creates a market for single-model ASICs. We are a fully in-person team in Cupertino, and greatly value engineering skills. We do not have boundaries between engineering and research, and we expect all of our technical staff to contribute to both as needed.

Requirements

Must-haves

Propose and conduct novel research to achieve results on Sohu that are unviable on GPUs
Translate core mathematical operations from the most popular Transformer-based models into maximally performant instruction sequences for Sohu
Develop deep architectural knowledge informing best-in-the-world software performance on Sohu HW, collaborating with HW architects and designers.
Co-design and finetune emerging model architectures for highest efficiency on Sohu
Guide and contribute to the Sohu software stack, performance characterization tools, and runtime abstractions by implementing frontier models using Python and Rust.
Representative projects
Propose and implement a novel test time compute algorithm that leverages Sohu’s unique capabilities to unlock a product could never be achieved on a typical GPU
Implement diffusion models on Sohu to achieve GPU-impossible latencies that allow for real-time image generation
Optimize model instructions and scheduling algorithms to optimize for utilization, latency, throughput, and/or a mix of these metrics.
Implement model-specific inference-time acceleration techniques such as speculative decoding, tree search, KV cache sharing, priority scheduling, etc by interacting with the rest of the inference serving stack.
You may be a good fit if you have
An ML Research background with interests in HW co-design
Experience with Python, Pytorch, and / or JAX
Familiarity with transformer model architectures and/or inference serving stacks (vLLM, SGLang, etc.) and/or experience working in distributed inference/training environments
Experience working cross-functionally in diverse software and hardware organizations
Strong candidates may also have
ML Systems Research and HW Co-design backgrounds
Published inference-time compute research and/or efficient ML research
Experience with Rust
Familiarity with GPU kernels, the CUDA compilation stack and related tools, or other hardware accelerators

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythonrustrestml

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision