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AI Squared

Sales Engineer

Mountain View, CA; Menlo ParkUnited States
Sales
POSTED 2 DEC 2025VERIFIED 6H AGO
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Role
341 days opentrue age
Posted 2 Dec 2025
Pay
$161k–$239kno pay posted
Market p25–p75
$161k–$239k
SF Bay Area · n=1752 · 365d to 6 Nov
CompanyAI Squared →
8open roles
Opened · 30d
+0
Closed · 30d
−0
2d of snapshots · 30d window
Market
37,431open · Software engineering
Opened · 7d
+1,5154% of open
All software engineering
74,988open
All tracked boards · live 9 Nov · n=37,431
AWSGCPAzureKubernetesDockerAirflow

About the Role

We are looking for a highly motivated Sales Engineer with a strong background in AI infrastructure to join our dynamic team. In this role, you will play a critical part in driving enterprise sales, supporting both pre-sales and post-sales activities, and partnering closely with account executives to deliver cutting-edge solutions to our clients.

Key Responsibilities

  • Leverage 10+ years of experience as a Sales Engineer to drive technical sales processes and customer success.
  • Sell AI-related infrastructure solutions to large and mid-sized enterprises, identifying client needs and aligning our solutions with their strategic goals.
  • Partner with Account Executives to enable account-based marketing and selling (ABM/ABS) strategies.
  • Operate as a self-starter, capable of working autonomously with minimal supervision in a fast-paced environment.
  • Support pre-sales activities including product demonstrations, proof-of-concepts, RFP responses, and technical deep dives.
  • Assist with post-sales enablement to ensure successful deployment and customer satisfaction.
  • Provide deep technical knowledge of cloud-native technologies, tools, and architecture best practices.
  • Demonstrate a strong understanding of AI and data pipelines, enabling clients to build scalable, intelligent solutions.

Qualifications

  • Proven experience in technical sales, ideally focused on AI, cloud, or data infrastructure.
  • Strong communication and presentation skills with the ability to influence both technical and business stakeholders.
  • Deep knowledge of cloud platforms (AWS, GCP, Azure), cloud-native ecosystems (Kubernetes, containers, CI/CD, etc.), and cloud-native AI tools and infrastructure—such as Amazon SageMaker, Google Vertex AI, Azure Machine Learning, Kubeflow, MLflow, and data pipeline orchestration tools like Apache Airflow and Argo Workflows.
  • Familiarity with machine learning workflows, MLOps tools, and data engineering best practices.
  • A proactive mindset and a customer-first attitude.
Source · AI SquaredFirst seen 4 NovOriginal ↗