ML Solutions Engineer

Our growing Customer Success team is looking for a passionate and enterprising Machine Learning Solutions Engineer. As an MLSE at Robust Intelligence, you will ensure our customers are successful and delighted throughout the customer lifecycle. This role is a highly visible technical expert who spans pre-sales and post-sales to drive customer acquisition, adoption, and expansion.

For prospective customers, you will partner with our Sales team as the technical lead, responsible to tailor product demonstrations that address business needs, overcome technical objections, and run high-quality, differentiated proofs of concept that establish the value of the RI platform. For signed customers, you will partner with our Customer Success team to drive onboarding, adoption, and expansion. You will serve as the customer’s primary technical point of contact, advocating for their needs internally and coordinating across multiple Robust Intelligence teams as needed to ensure our customers thrive.

What you will do

• Build strong, meaningful relationships with our customers and proactively seek to understand their technical and business objectives
• Drive technical engagement and adoption across the complete customer journey
• Champion customer needs internally, advocating for them within Robust Intelligence and leveraging your customer knowledge to influence the product roadmap
• Coordinate across multiple Robust Intelligence teams, including Product, Engineering, Sales, and Support to advance adoption, expansion, and other Customer Success priorities
• Become a power user of the RI platform: deeply understand the user experience (using the APIs, navigating the GUI, integrating with different platforms, etc.)
• Create and deliver tailored product demonstrations that address technical pain and show business value
• Design high-quality, differentiated proofs of concept that establish the value of the Robust Intelligence platform
• Scope customer engagements; investigate and understand customer needs
• Build bespoke machine learning validation solutions using the RI platform
• Work with customers to architect integrations with their existing machine learning stack
• Work with customers to design best practices, processes, and frameworks for their organization
• Gather, understand, and communicate customer feedback to RI’s Product and Engineering teams
• Own customer-facing technical communications to ensure a professional and concise message

What you bring to the team

• At least five years of professional experience with machine learning, ML operations, data science, or a related field
• Experience building ML models and putting them into production
• Strong expertise in AI, ML, data science, or related spaces; general knowledge of statistics and applied ML
• Knowledge of Python (or other programming languages)
• Significant knowledge of the artificial intelligence ecosystem; a deep understanding of how data scientists and machine learning engineers develop and deploy AI models
• Experience with tooling and platforms in the ML ecosystem (Huggingface, MLFlow, Databricks, DataRobot, etc.)
• Self-motivated and excited to work at a startup; comfortable with ambiguity, eager to learn, and able to thrive with little to no direct, daily oversight
• Strong communication skills (business fluency in spoken and written English)
• Ability to travel about 25% of the time to meet with your accounts

Nice to have

• Experience in customer success, sales, sales engineering, consulting, or other customer-facing fields
• Familiarity with customer success and sales processes
• Knowledge of REST, Kubernetes, Docker, and Unix
• Familiarity with major public cloud platforms (e.g. AWS, Google Cloud Platform, Azure) and their ML products (AWS Sagemaker, GCP Vertex AI, etc.)
• Ability to establish trust at the CxO level as well as with technology practitioners
• Experience developing and delivering product demonstrations and leading proofs of concept
• Bachelor’s degree (or equivalent) in computer science, engineering, mathematics, statistics, or another quantitative field