Data Engineer (AI)

Формат работы

onsite

Тип работы

fulltime

Грейд

senior

Английский

Страна

Вакансия из Hirify Global, списка международных tech-компаний
Для мэтча и отклика нужен Plus

Мэтч & Сопровод

Для мэтча с этой вакансией нужен Plus

Описание вакансии

Текст:

TL;DR

Data Engineer (AI) (Python/Go, Spark/Kafka): Design and maintain petabyte-scale batch and streaming data pipelines for robotics foundation model training and evaluation with an accent on data infrastructure, distributed systems, and unified real-world and synthetic datasets. Focus on building production-grade storage, ingestion, transformation, and orchestration layers for embodied AI systems.

Location: Bay Area, United States; work arrangement: on-site

Company

hirify.global builds general-purpose Physical AI and robotics foundation models.

What you will do

Design, build, and maintain petabyte-scale batch and streaming data pipelines for robotics foundation model training and evaluation.
Own the core data infrastructure, including data models, storage systems, ingestion pipelines, transformation frameworks, and orchestration layers.
Standardize data models and unify processing pipelines across real-world teleoperation and synthetic simulation datasets.
Collaborate with engineering and research specialists building general-purpose Physical AI.

Requirements

Excellent software engineering skills in Python, Go, or a similar language.
8+ years of experience designing, building, and maintaining large-scale data pipelines.
Deep understanding of distributed systems, including Spark, Kafka, or similar technologies.
Extensive experience with data lakes, warehouses, and object stores such as S3.
Experience running and maintaining production-grade infrastructure with Kubernetes and Terraform.

Nice to have

Experience supporting AI systems, particularly embodied AI such as self-driving systems.

Culture & Benefits

Work on-site with a team focused on general-purpose Physical AI.
Collaborate across engineering and research on robotics foundation model infrastructure.

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