Senior Technical Marketing Engineer

United StatesRemotesenior

OtherTechnical Marketing Engineer

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Quick Summary

Key Responsibilities

Reference Architectures: Design, build, and validate reference architectures that show how MinIO deploys in real AI and analytics environments,

Requirements Summary

Produce technical blogs, deployment guides, solution briefs, and hands-on labs aimed at an architect and platform-engineering audience, written in a precise,

Technical Tools

OtherTechnical Marketing Engineer

MinIO is the industry leader in high-performance object storage and the company behind the world’s fastest, most widely deployed object store, powering production infrastructure for more than half of the Fortune 500, including 9 of the 10 largest global automakers and all 10 of the largest U.S. banks. Our enterprise offering, AIStor, is engineered to handle the scale, speed, and pressure of modern AI and analytics, from terabytes to exabytes, all in a single namespace.

The Senior Technical Marketing Engineer builds the technical proof behind MinIO's story. This is a hands-on engineering role that sits between Product, Engineering, and the field. You will design and validate reference architectures, run and document benchmarks, build reproducible demos, and produce the technical content that lets architects and platform engineers evaluate MinIO on the merits. Where product marketing defines what the products do and why they win, you make it real and measurable.

MinIO's portfolio spans objects, tables, and context memory, anchored by AIStor as the data foundation for enterprise AI and MemKV as the context memory layer for inference. You will work across that surface, in close partnership with Engineering and with technical counterparts at hardware and ecosystem partners, to turn integration work and performance results into credible, reproducible artifacts.

Responsibilities

~2 min read

→Reference Architectures: Design, build, and validate reference architectures that show how MinIO deploys in real AI and analytics environments, including configurations aligned to enterprise and partner architecture standards. Document them so a customer can reproduce the result.
→Benchmarking and Performance Validation: Plan, run, and document benchmarks across AIStor and MemKV. Report results with full configuration and workload context, for example aggregate throughput in a stated cluster configuration or microsecond retrieval of cached context, so every number is defensible and repeatable.
→Demos and Proofs of Concept: Build reproducible demos and proof-of-concept environments that demonstrate MinIO across the AI pipeline, from ingest and training data lakes to large-context serving and KV cache offload. Make complex capabilities easy to see and easy to rerun.
→Integration Validation: Validate and document MinIO's integrations with the modern AI infrastructure stack, including inference frameworks, GPU-accelerated platforms, networking, and partner hardware. Surface what works, what the requirements are, and what the measured impact is.
→Technical Content Creation: Produce technical blogs, deployment guides, solution briefs, and hands-on labs aimed at an architect and platform-engineering audience, written in a precise, peer-to-peer register with claims qualified by configuration and workload.
→Partner Co-Engineering Support: Work hands-on with partner technical teams on joint validation, lab benchmarks, and co-engineered solutions, supporting the co-engineered, not co-marketed standard with real, reproducible results.
→Field and SE Enablement: Build and deliver deep technical enablement for sales engineers and account teams, including technical briefings, demo assets, and deployment guidance, and provide hands-on support in complex evaluations and proofs of concept.
→Customer-Facing Technical Engagement: Present architectures, benchmarks, and demos directly to technical buyers and architects, and bring field and customer feedback back into content, demos, and product input.

Requirements

~2 min read

Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent hands-on experience.
5+ years in hands-on technical roles such as solutions architecture, systems or sales engineering, performance engineering, infrastructure engineering, or technical marketing engineering, with at least 3 years working on AI/ML infrastructure, storage, or data platforms.
Strong hands-on systems and infrastructure skills, including Linux, networking, containers and Kubernetes, and distributed storage or data systems.
Ability to build demos and test harnesses, write scripts and automation, and design and run benchmarks that produce reproducible, well-documented results.
Strong technical writing skills, with the ability to explain complex systems clearly to a technical audience and qualify performance claims accurately.
Proven ability to work cross-functionally with Engineering, Product Management, and the field, and to operate independently in a fast-paced, remote-first environment.
Hands-on familiarity with the S3 API, object storage, or high-performance storage for AI and analytics workloads.
Experience with GPU-accelerated AI infrastructure, RDMA networking, GPUDirect Storage, or DPU-based data paths.
Familiarity with inference frameworks and the modern AI serving stack, including frameworks such as vLLM, SGLang, and TensorRT-LLM, and with KV cache behavior and memory tiering.
Experience building reference architectures or running lab validation alongside hardware or ecosystem partners.
Background that includes time in customer-facing technical roles, such as technical pre-sales or developer relations.