Machine Learning Sales Engineer

Arize is seeking a Sales Engineer to join their team and help bring their machine learning observability platform to every machine learning organization. The Sales Engineer will work alongside Account Executives to articulate the value of the Arize platform and develop a compelling value proposition. The ideal candidate will have strong communication skills, be a quick and self-learner, and have knowledge of machine learning. The Sales Engineer will also act as a domain expert within AI/ML, engaging in relevant ML communities online to raise awareness on challenges of deploying ML in production. Arize's mission is to make the world's AI work and work for the people.

• Build relationships with technical stakeholders
• Lead product demonstrations of the Arize platform
• Lead discovery to understand prospect’s ML stack to collaborate with the Sales team to construct a compelling value proposition of the Arize Platform
• Handle technical objections and develop strategies across sales, engineering, and product to unblock them
• Write educational and compelling blog posts about ML and MLOps related topics
• Collaborate to create and enhance documentation, recorded video assets and other publicly available as well as internal enablement materials
• Engage in relevant ML communities online to raise awareness on challenges of deploying ML in production

• Build relationships with technical stakeholders
• Lead product demonstrations of the Arize platform
• Lead discovery to understand prospect’s ML stack to collaborate with the Sales team to construct a compelling value proposition of the Arize Platform
• Handle technical objections and develop strategies across sales, engineering, and product to unblock them
• Write educational and compelling blog posts about ML and MLOps related topics
• Collaborate to create and enhance documentation, recorded video assets and other publically available as well as internal enablement materials
• Engage in relevant ML communities online to raise awareness on challenges of deploying ML in production
• Strong Communication Skills: empathize with the frame of reference of who you are communicating with and tailor your message and approach accordingly, ability to simplify complex, technical concepts
• A quick and self learner: undaunted by the technical complexity of production ML deployments and welcome the challenge to learn about them and develop your own POV, ask the right questions with the customer to uncover nuances in their unique deployments, ability to work within ambiguity and take action with limited direction
• Knowledgeable in Machine Learning: know the difference between Linear Regression and Boosted Trees and the advantages / disadvantages of each, have some experience training models in common packages such as scikit-learn, HuggingFace, fastai, and etc.
• 5+ years within customer facing role: pre-sales, technical account management, or consulting experience, worked customers within the high enterprise (e.g. Fortune 500, etc)
• Proficiency in: Python, Linux/Unix
• Bonus Points, But Not Required: previous experience working across and aligning Sales, Product, and Engineering, previous experience within a team that underwent a high growth stage, previous engineering experience in: Data Engineering, MLOps, Kubernetes, GCP / AWS / Azure