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

  • Collaborate closely with new Arize customers as they onboard onto Arize. You will work with customers to advise on:
    • Arize use cases
    • Data integration methods
    • ML observability and MLOps best practices
  • 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 for onboarding
  • Collaborate with Customer Success Engineers to understand and execute against the overall goals for the onboarding accounts

What We’re Looking For

  • 1-2 years experience in a customer facing role
  • Experienced cloud computing environments (AWS, Azure, GCP)
  • Experienced in a programming language (Python, R, Java, Go, etc), and SQL
  • 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
  • Strong Communication Skills – Ability to simplify complex, technical concepts
  • A quick and self learner – undaunted by technical complexity of production ML deployments and welcome the challenge to learn about them and develop your own POV

Bonus Points, But Not Required

Customer facing experience strongly preferred such as Solutions Architect, Onboarding Specialists, Sales Engineer, Customer Success Engineer, Consultant, or Professional Services roles.

The estimated annual salary and variable compensation for this role is between $90,000 – $150,000, 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.

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