Cedana (YC S23) Is Hiring
Cedana is addressing the challenges of AI and HPC infrastructure by enhancing cluster utilization and reliability through automated GPU checkpointing. The company is seeking a Forward Deployed Engineer to lead customer integrations and optimize platform performance. The role requires extensive experience with SLURM deployments and strong Linux fundamentals.
- ▪Cedana maximizes AI+HPC cluster utilization and reliability with automated GPU checkpointing infrastructure.
- ▪The Forward Deployed Engineer will engage with customers to deploy Cedana in various environments, including SLURM and Kubernetes.
- ▪Candidates should have 3-10 years of software engineering experience and a strong understanding of SLURM and Linux systems.
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| Original publisher | Y Combinator |
| Canonical URL | https://www.ycombinator.com/companies/cedana/jobs/d1vYocG-forward-deployed-engineer-ai-hpc |
| Publication time | Fri, 29 May 2026 12:01:00 +0000 |
| Retrieval time | 2026-05-29T12:25:00.645Z |
| Last seen | 2026-05-29T12:25:00.645Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | BmAjnXUaN9Y_ |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Introducing Cedana The Problem AI and HPC infrastructure suffers from scarcity and high costs, so when failures happen they are costly in terms of time and money. Cluster productivity directly determines research output and revenue. Achieving high utilization and throughput is increasingly challenging due to the complexity of workloads, hardware, and operations. Cedana’s Solution Cedana maximizes AI+HPC cluster utilization and reliability with automated GPU checkpointing infrastructure. We enable transparent and fast migration of GPU workloads across instances, without losing work. Workloads automatically migrate to achieve new levels of reliability and throughput while accelerating time to results.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Y Combinator.
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