ML Solutions Engineer

The Opportunity

AI is rapidly changing the world. From processing job applications and credit decisions, to making content recommendations and helping researchers analyze genetic markers at scale -- many aspects of our daily lives are touched by machine learned systems in some way. 

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

The Team

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 client-obsessed ML Solutions Engineer with entrepreneurial tendencies to join the good fight and help build out our Solutions Engineering org. You’ll be the trusted technical advisors for our customers, driving business value, offering advice, and growing accounts. You’ll accomplish this by leading customers to solutions oftentimes by teaching the product to new users or consulting on best practices. You must be ready for technical discussions with data scientists and engineers, then demonstrate the value of Arize in business discussions with directors and executives. The goal is to enable our customers to become successful and enthusiastic about Arize.

What You’ll Do

• You will act as a trusted advisor to our customers, while also building relationships with technical stakeholders
• You will act as the “Voice of the Customer” - Regularly engaging them on status calls, educating on product roadmap and QBRs, managing escalations, while influencing our roadmap in partnership with our Product team.
• Interface with our pre-sales engineering team to gather client goals and KPI’s.
• Spearhead new opportunities in which Arize can provide the most value that will drive renewals and new accounts.

What We’re Looking For

• Previous experience working as a Data Scientist, Machine Learning Engineer, or as an Engineer working with ML models in production.
• Comfortable with Kubernetes and public Cloud environments (AWS, Azure, GCP)
• Knowledge of machine learning frameworks such as TensorFlow, PyTorch or Scikit-learn
• Understanding of ML/DS concepts, model evaluation strategies and lifecycle (feature generation, model training, model deployment, batch and real time scoring via REST APIs) and engineering considerations
…