About us

MangoBoost, Inc.

Agentic AI is fundamentally reshaping the paradigm of AI infrastructure. As environments emerge where countless AI agents operate simultaneously, conventional data center architectures are reaching their limits in both performance and efficiency. In response to this shift, MangoBoost is pioneering a new approach with its full-stack AI infrastructure solution designed to innovate every layer of the AI data center.

From LLMBoost, our AI inference optimization software, to GPU and storage systems powered by our proprietary DPU architecture, and orchestration software that seamlessly integrates and manages the entire stack, MangoBoost designs and develops every layer of AI infrastructure in-house. Rather than building isolated products, we focus on eliminating system-wide bottlenecks and fundamentally addressing inefficiencies across the entire infrastructure stack.

MangoBoost’s technology minimizes data movement and coordination overhead while maximizing overall system resource utilization, enabling exceptional performance and optimized total cost of ownership (TCO) for customers. Today, our solutions are already being deployed within real-world AI infrastructure environments through global partners, validating the strength of our technology. Through these efforts, MangoBoost is establishing a new global standard for AI data centers.

Position Overview

MangoBoost is engineering the full-stack AI infrastructure driving next-generation, hyper-scale AI data centers—spanning advanced AI networking, next-gen GPU systems, high-performance storage, and the full-stack software ecosystem.

Within this mission, our team is building a high-performance storage appliance purpose-built for AI workloads: an open-source distributed storage backend orchestrated on Kubernetes, engineered to deliver data-center-scale IO performance (SPDK, NVMe-oF data path; Kubernetes Operator orchestration; REST API/CLI control plane; React dashboard; integrated with DAOS, Kuzu, Qdrant).

As a Software Validation Engineer, you own the performance, quality, and reliability of this platform. You will architect the automated frameworks that prove the appliance meets data-center-scale performance targets and rock-solid reliability—building benchmark pipelines, stress/fault-injection harnesses, and multi-node test clusters, and root-causing deep system issues alongside the development team. We hire from junior to senior.

A note on languages: our stack is primarily Go and C/C++, but we treat AI-assisted development as a first-class workflow. If you can build correct, effective validation tooling with the help of modern AI coding agents, the specific language you've used before is not a barrier—your grasp of systems fundamentals and rigorous methodology is what matters.

Responsibilities & Opportunities

Architect, code, and operate automated validation and performance-evaluation pipelines across multi-node storage clusters
Build benchmark, stress, and fault-injection frameworks to characterize throughput, latency, and behavior under load and failure
Reproduce, isolate, and root-cause complex defects across the data path, orchestration, and control plane—collaborating directly with developers
Define and run industry-standard storage benchmarks and drive competitive analysis against leading market solutions
Validate reliability and high-availability behavior (node/disk/network failures, recovery) through clear, outcome-oriented scenarios
Document methodology, results, and regressions in English

Required Qualifications

BS, MS, or PhD in CS, EE, CE, or equivalent practical experience (junior to senior welcome)
Solid understanding of storage and/or networking system concepts, with hands-on project experience in related systems
Experience with performance benchmarking, bottleneck analysis, and software-driven root-cause debugging on real, bare-metal systems
Test automation / validation tooling development, with scripting proficiency (Python, Bash, or similar)
Working knowledge of Linux system fundamentals
Ability to write technical documentation and communicate effectively in English

Preferred Qualifications

Hands-on storage/IO benchmark experience: IO500, fio, SPECstorage, MLPerf Storage
Experience with distributed/parallel storage (Ceph, Lustre, DAOS) and high-speed fabrics (NVMe-oF, RDMA/RoCE, InfiniBand)
CI/CD and test orchestration on Kubernetes
Fault-injection / chaos engineering / HA & failover validation experience
Experience with LLM inference systems (vLLM, LMCache, Mooncake) or AI storage workloads
Familiarity with SPDK or other kernel-bypass storage stacks

Notice Regarding Return of Hiring Documents

This notice is in accordance with Article 11, Section 6 of the Fair Hiring Procedure Act, which allows job applicants, excluding the final successful candidate, to request the return of any recruitment documents they have submitted.
Job applicants who were not selected as the final candidates in the recruitment process are entitled to request the return of the recruitment documents they submitted within 180 days of the date the employment decision is confirmed. However, this does not apply to documents submitted via the website or email, or documents voluntarily submitted by the applicant without the company’s request. If the recruitment documents are lost due to natural disasters or other reasons for which the company is not responsible, it will be considered as having been returned.
Applicants wishing to request the return of their recruitment documents should fill out the Request for Return of Hiring Documents [Form 3 of the Enforcement Rules of the Fair Hiring Procedure Act] and submit it by email (recruiting@mangoboost.io). The documents will be sent by registered mail to the designated address within 14 days from the confirmation of submission. Please note that the cost of registered mail will be borne by the recipient.
The company will keep the recruitment documents for 180 days following the confirmation of the employment decision. If no request for the return of documents is made within that period, the company will dispose of all recruitment documents without delay in accordance with the Personal Information Protection Act.