Systems Software Engineer - ML Inference & GPU Clusters

raydar

San Francisco (CA)

In loco

USD 200.000 - 300.000

Tempo pieno

4 giorni fa
Candidati tra i primi
Generatore di candidature

Una candidatura fatta su misura per questo lavoro — un curriculum e una lettera di presentazione personalizzati, perfettamente in linea con l'annuncio.

Supera i filtri ATS

Vantaggi offerti da questo lavoro

Health coverage
Dental coverage
Vision coverage
Unlimited PTO
Parental leave

Descrizione del lavoro

Raydar is seeking a Systems Software Engineer to work on low-level performance and infrastructure for a production ML serving platform in San Francisco, CA. You will tune software for speed, run large compute clusters, and interact with customers in a high-autonomy, high-ownership team.

Responsibilities include delivering fast support for new models, improving the serving stack with batching and quantization, writing GPU kernels, and operating diverse compute clusters in a cost-efficient,

Competenze

  • Bachelor's degree in Computer Science or equivalent.
  • Strong background in backend/infrastructure or systems engineering.
  • Excellent communication with customers and cross-functional teams.

Mansioni

  • Deliver fast support for newly released open-source models, tuned for low latency and high throughput.
  • Improve the model serving stack with batching, caching, decoding strategies and quantization.
  • Write and tune low-level GPU kernels across several hardware vendors.
  • Design, deploy and operate compute clusters built from mixed hardware.
  • Run production inference at scale with reliability and cost efficiency; engage with customers.

Conoscenze

Low-level systems
Backend experience
Customer-facing
CS degree

Formazione

Bachelor's degree in Computer Science

Descrizione del lavoro

Raydar is seeking a Systems Software Engineer to work on low-level performance and infrastructure for a production ML serving platform in San Francisco, CA. You will tune software for speed, run large compute clusters, and interact with customers in a high-autonomy, high-ownership team.

Responsibilities include delivering fast support for new models, improving the serving stack with batching and quantization, writing GPU kernels, and operating diverse compute clusters in a cost-efficient,

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