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Engineer, Supercomputing & Distributed Systems

Krea - San Francisco, California, United States

Posted Apr 3, 2026

Benefits

Parental leave
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Non-birth-parent leave
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Family-building benefits
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  • Adoption assistance: Not verified
  • Surrogacy assistance: Not verified
Mental health support
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Relocation assistance
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Childcare support
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Learning budget
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Verification
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Salary
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401(k) match
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Market context

U.S. role benchmark (BLS OEWS)
$116,543 U.S. median for this role
Projected growth (BLS Employment Projections)
+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function
Engineering From the posting source checked Jun 20, 2026
Seniority
Mid From the posting source checked Jun 20, 2026

Schedule

Shift type
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Weekend work
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Company

Company stage
Growth-stage From the posting source checked Jun 20, 2026

Application

Cover letter
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Assessment
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Deadline
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Where they hire

State eligibility is not yet verified.

About this role

Engineer, Supercomputing & Distributed Systems San Francisco, California, United States About Krea At Krea, we are building next-generation AI creative tools. We are dedicated to making AI intuitive and controllable for creatives. Our mission is to build tools that empower human creativity, not replace it. We believe AI is a new medium that allows us to express ourselves through various formats-text, images, video, sound, and even 3D. We're building better, smarter, and more controllable tools to harness this medium. Supercomputing / AI Infra at Krea We build and operate the infrastructure for Krea's research and inference. Distributed training, 1000+ K8s GPU clusters, petabyte scale data pipelines, etc. We build a lot of this from scratch - custom distributed datastores, job orchestration systems, and streaming pipelines that replace tools like Kafka and Ray for modern AI workloads at scale. Example projects: Distributed data systems - Design multi-stage pipelines that turn petabytes of raw data into clean, annotated datasets - Run classification models on billions of images - Deploy and combine LLMs to caption massive multimedia data GPU infrastructure - Manage distributed training and inference on 1000+ GPU Kubernetes clusters - Solve orchestration and scaling for large-scale GPU job processing - Scale workloads and research between clusters in multiple datacenters Distributed training - Profile and optimize dataloaders streaming thousands of images per second - Profile and debug InfiniBand networking on huge training runs - Build fault tolerance systems for large-scale pretraining - Collaborate with researchers on evolving RL infrastructure Applied ML pipelines

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