Member of Technical Staff

Digitailor; San Francisco (CA); Presencial; USD 200 000 - 300 000; Tempo integral

Resumo da oferta

Digitailor in San Francisco, CA, is seeking an engineer to build AI infrastructure for open-source model inference. You will work across models, serving systems, kernels, and production infrastructure to deliver reliable, cost-efficient inference across diverse hardware.

You will own day-zero model integration, optimize latency and throughput, implement batching, KV caching, and quantization, and write CUDA/HIP/Triton kernels while collaborating with customers and the engineering team.

Qualificações

• 1–6 years in systems, backend, or infra engineering.
• Strong CS fundamentals and experience at a high-caliber tech org or startup.
• Familiarity with CUDA, HIP, Triton or GPU tooling; inference optimization a plus.

Responsabilidades

• Deliver day-zero support for new open-source models and tune for latency/throughput.
• Optimize serving with batching, KV caching, speculative decoding, and quantization.
• Write and tune CUDA/HIP/Triton kernels across accelerator platforms.
• Design, deploy, and operate heterogeneous clusters across providers.
• Run production inference on mixed hardware fleets; improve reliability and cost per token.

Conhecimentos

Systems/Backend engineering

Infrastructure

Low-level programming

GPU programming

Python

Ferramentas

CUDA

HIP

Triton

Descrição da oferta de emprego

About The Role

Join a small team building AI infrastructure that improves the performance and efficiency of open-source model inference. You will work across models, serving systems, kernels, and production infrastructure to help deliver reliable, cost-effective inference across diverse hardware.

What You'Ll Do

Deliver day-zero support for new open-source models, tuning them for latency and throughput. Optimize serving systems through techniques such as batching, KV caching, speculative decoding, and quantization. Write and tune kernels using CUDA, HIP, and Triton across a range of accelerator platforms. Design, deploy, and operate heterogeneous clusters across infrastructure providers. Run production inference across mixed hardware fleets, improving reliability, observability, and cost per token. Work with customers as well as the engineering team to identify problems and guide solutions.

What We'Re Looking For

One to six years of experience, with strong systems, backend, infrastructure, or low-level engineering work. Strong understanding of computer architecture, memory hierarchies, and operating system internals. Experience owning complex systems end to end, notable technical achievements, or a record of rapid progression. Strong undergraduate computer science fundamentals and experience at a high-caliber technology organization or early-stage startup. Familiarity with CUDA, HIP, Triton, Python, or GPU programming; experience with inference optimization and large-scale infrastructure is valuable. Experience using AI agents and coding tools to improve engineering workflows, and comfort communicating with customers. Available to start immediately and work on-site in San Francisco. Compensation & Benefits Base salary is $200,000 to $300,000 USD annually. Visa sponsorship is available.

Location

On-site in San Francisco, California, United States.