Jobs in Ingolstadt, Germany

AI is rapidly transforming the world. Whether it’s developing the next generation of human-level intelligence, enhancing voice assistants, or enabling researchers to analyze genetic markers at scale, AI is increasingly integrated into various aspects of our daily lives.

Arize is the leading AI observability and Evaluation platform to help AI teams discover issues, diagnose problems, and improve the results of their AI Applications. In short: we are here to build world class software that helps make AI applications work better.

Our engineering team builds systems that interact with some of the most complex software ever deployed in production. The team is composed of industry veterans that have built deep learning infrastructure, autonomous drones, ridesharing marketplaces, ad tech and much more. We are looking for a Sales Engineer to join the good fight and get this product in the hands of every Machine Learning organization.

You'll work side-by-side with our Account Executives to articulate the Arize value and overall strategy to differentiate the platform throughout the sales process and drive POCs from requirements gathering to close. The activities performed by a Sales Engineer are diverse - one day you might be assisting with a high-level pitch of Arize to a CDO, and the next day you might be working hands-on to develop a small proof of concept.

You are the trusted advisor to the customer:

• 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

Act as a Domain Expert within AI/ML:

• 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:

• You can 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:

• You are undaunted by the technical complexity of production ML deployments and welcome the challenge to learn about them and develop your own POV.
• You 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:

• You may not have a PhD in ML but you know the difference between Linear Regression and Boosted Trees and the advantages / disadvantages of each.
• You 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

The estimated annual salary and variable compensation for this role is between $140,000 - $200,000, plus a competitive equity package. Actual compensation is determined based upon a variety of job related factors that may include: transferable work experience, skill sets, and qualifications. Total compensation also includes a comprehensive benefit package, including: medical, dental, vision, 401(k) plan, unlimited paid time off, generous parental leave plan, and others for mental and wellness support.

While we are a remote-first company, we have opened offices in New York City and the San Francisco Bay Area, as an option for those in those cities who wish to work in-person. For all other employees, there is a WFH monthly stipend to pay for co-working spaces.