Senior Technical Marketing Engineer

Role Overview

• 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

• 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.

Tech Stack

• Kubernetes
• Linux

Benefits

• Health Care Plan (Medical, Dental & Vision)
• 401K with 3% Contribution
• Pre-IPO Stock Options
• At least 12 Public Holidays
• Flexible Time Off