Inference Software Engineer

Etched.ai, Inc.; San Jose (CA); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Medical, dental, and vision packages

Housing subsidy of $2k per month

Relocation support

Various wellness benefits

Daily lunch + dinner in the office

Job summary

Etched.ai, Inc. is looking for candidates to support porting AI models to its innovative architecture in San Jose, California. Responsibilities include enhancing runtime environments and optimizing performance layers. Ideal applicants are proficient in C++ or Rust and have experience with distributed software systems. Benefits include a housing subsidy, relocation support, and comprehensive health packages. Join a fully in-person team that values engineering and research collaboration.

Qualifications

• Experience with complex distributed software systems and performance-sensitive applications.
• Ported applications to non-standard accelerator hardware or platforms.
• Developed low-latency applications using user-space networking.

Responsibilities

• Support porting state-of-the-art models to our architecture.
• Build and enhance Sohu’s runtime for multi-node inference.
• Analyze performance profiling and optimize communication layers.

Skills

C++ proficiency

Rust proficiency

Understanding of Linux internals

Familiarity with PyTorch

Familiarity with JAX

Job description

About Etched

Etched is building the world’s first AI inference system purpose-built for transformers - delivering over 10x higher performance and dramatically lower cost and 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. Backed by hundreds of millions from top-tier investors and staffed by leading engineers, Etched is redefining the infrastructure layer for the fastest growing industry in history.

Key responsibilities

• Support porting state-of-the‑art models to our architecture. Help build programming abstractions and testing capabilities to rapidly iterate on model porting.


• Build, enhance, and scale Sohu’s runtime, including multi-node inference, intra-node execution, state management, and robust error handling.


• Optimize routing and communication layers using Sohu’s collectives.


• Utilize performance profiling and debugging tools to identify bottlenecks and correctness issues.

You may be a good fit if you have

• Proficiency in C++ or Rust.


• Understanding of performance-sensitive or complex distributed software systems like Linux internals, accelerator architectures (e.g. GPUs, TPUs), Compilers, or high-speed interconnects (e.g. NVLink, InfiniBand).


• Familiarity with PyTorch or JAX.


• Ported applications to non-standard accelerator hardware or hardware platforms.

Strong candidates may also have experience with (Nice-to-have qualifications)

• Developed low‑latency, high-performance applications using both kernel-level and user-space networking stacks.


• Deep understanding of distributed systems concepts, algorithms, and challenges, including consensus protocols, consistency models, and communication patterns.


• Solid grasp of Transformer architectures, particularly Mixture-of-Experts (MoE).


• Built applications with extensive SIMD (Single Instruction, Multiple Data) optimizations for performance-critical paths.

Benefits

• Medical, dental, and vision packages with generous premium coverage
  
  
  
  • $500 per month credit for waiving medical benefits
  
  
  
  


• Housing subsidy of $2k per month for those living within walking distance of the office


• Relocation support for those moving to San Jose (Santana Row)


• Various wellness benefits covering fitness, mental health, and more


• Daily lunch + dinner in our office

How we’re different

Etched believes in 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 San Jose (Santana Row), 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.