Director of Technical Product Management, Inference as a Service

Summary:

MARA is seeking a Director of Technical Product Management to lead the strategy, development, and deployment strategy for our IaaS platform. You'll bridge technical, operational, and customer-facing teams to build scalable inference infrastructure that supports real-world AI applications. Your focus will be on enabling clients to deploy and scale ML models on dedicated compute with maximum performance and uptime.

ESSENTIAL DUTIES AND RESPONSIBILITIES include the following. Other duties may be assigned.

· Define and drive the product vision and roadmap for our Inference as a Service offering.

· Gather and prioritize customer requirements from AI developers, enterprise users, and internal stakeholders.

· Collaborate with engineering, data center ops, and energy teams to ensure the compute infrastructure meets IaaS needs (e.g., GPU availability, latency targets).

· Work closely with sales and business development to support customer onboarding and ensure alignment with SLAs.

· Identify market trends, emerging AI inference patterns (LLMs, vision, etc.), and integrate them into product strategy.

· Define product KPIs, collect usage data, and iterate quickly based on performance insights.

· Ensure the IaaS platform meets compliance, cost-efficiency, and environmental goals.

QUALIFICATIONS: To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions.

· 8–12+ years of product management, ideally in data infrastructure, cloud services, or AI/ML domains.

· Strong technical understanding of ML inference workloads and GPU/accelerator environments.

· Proven experience launching infrastructure products or platforms used by technical teams.

· Excellent communication and stakeholder management skills across engineering and non-technical functions.

· Experience with tools like Kubernetes, Triton, or model-serving platforms is a plus.