ML Solutions Engineer

Arize is seeking a client-focused ML Solutions Engineer to join their team. The ideal candidate will have experience working as a Data Scientist, Machine Learning Engineer, or Engineer working with ML models in production, as well as knowledge of machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn. The Solutions Engineer will act as a trusted advisor to customers, driving business value, offering advice, and growing accounts. They will also interface with pre-sales engineering teams to gather client goals and KPIs, and spearhead new opportunities in which Arize can provide the most value that will drive renewals and new accounts.

• Act as a trusted advisor to customers and build relationships with technical stakeholders
• Act as the "Voice of the Customer" and regularly engage with them on status calls, educate on product roadmap and QBRs, manage escalations, and influence the roadmap in partnership with the Product team
• Interface with the pre-sales engineering team to gather client goals and KPIs
• Spearhead new opportunities in which Arize can provide the most value that will drive renewals and new accounts
• 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
• Proficiency in a programming language (Python, R, Java, Go, etc)
• Strong communication skills - 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.

• 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
• Proficiency in a programming language (Python, R, Java, Go, etc)
• 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.
• Previous engineering experience in Data Science, MLOps, or ML Frameworks (Bonus Points, But Not Required)