Solutions Engineer

Paralleliq; United States; Remote; USD 110,000 - 165,000; Full time

Benefits offered by this job

Remote work (US)

Job summary

Paralleliq is seeking a Solutions Engineer to help customers evaluate, adopt, and optimize a technical product aimed at understanding and reducing GPU workload waste. You will guide deployments from initial infrastructure access to actionable findings, translating customer needs into product and engineering work.

You will lead onboarding, investigate GPU usage, surface practical improvements, and work with engineering to address product gaps.

Qualifications

• Three+ years in solutions engineering, customer engineering, or technical account management.
• Hands-on with Kubernetes and a major cloud platform (AWS/GCP/Azure).
• Familiarity with AI/ML infrastructure including model serving, GPU utilization, or inference optimization.
• Ability to read Python and YAML and write light scripts for customer environments.
• Strong communication across technical discussions and executive demos.
• Experience building processes and playbooks for early-stage products.

Responsibilities

• Lead customer onboarding, including environment access, deployment, and surfacing initial recommendations.
• Investigate GPU usage and waste in customer environments, then explain practical opportunities to improve utilization.
• Collaborate with engineering to address product gaps uncovered during deployments.
• Create repeatable onboarding guides, playbooks, and technical documentation.
• Lead discovery conversations and technical demonstrations for prospective cloud providers and enterprise AI teams.

Skills

Kubernetes

Cloud platforms

AI/ML infra

Python & YAML scripting

Communication

Early-stage products

Job description

Role overview

Help customers evaluate and adopt a technical product for understanding and reducing waste in GPU workloads. You’ll guide deployments from initial infrastructure access to useful findings, while connecting customer needs with product and engineering teams.

Responsibilities

• Lead customer onboarding, including environment access, deployment, and surfacing initial recommendations.
• Investigate GPU usage and waste in customer environments, then explain practical opportunities to improve utilization.
• Work with engineering to address product gaps uncovered during deployments.
• Create repeatable onboarding guides, playbooks, and technical documentation.
• Lead discovery conversations and technical demonstrations for prospective cloud providers and enterprise AI teams.

Requirements

• At least three years in solutions engineering, customer engineering, or technical account management.
• Practical experience with Kubernetes and a major cloud platform such as AWS, GCP, or Azure.
• Familiarity with AI/ML infrastructure, including model serving, GPU utilization, or inference optimization.
• Ability to read Python and YAML and write light scripts for customer environments.
• Strong communication skills across technical discussions and executive demonstrations.
• Experience working with early-stage products and building processes where established playbooks are not yet available.

Benefits and work setup

• Full-time, remote role based in the US.