DataRobot

Sales Engineer

• Partner with Account Executives and sales leadership to run technical discovery, architect solutions, deliver compelling demos, and lead proof-of-value engagements
• Define key technical requirements, recommend deployment models that meet specific security mandates, and advise on data readiness
• Support conversations around AI governance, data security, model transparency, and responsible AI practices
• Provide structured feedback to Product, Marketing, and Enablement teams
• Act as a trusted advisor across the sales team by supporting Account Executives, Strategic Account Directors, and Business Development Reps with the technical knowledge and assets that advance AI adoption.

Senior Pre-Sales Applied AI Expert – Sales Engineering

Artificial Intelligence

• Collaborate closely with DataRobot customers and bring your expertise in data science and the DataRobot platform to help our customers achieve their organization’s objectives with AI.
• Foster an environment of autonomy where everyone is encouraged to identify and prioritize customer problems and opportunities.
• Lead data science implementation and help develop a Joint Success Plan (JSP).
• Support customers with problem framing, data prep, scripting, model building, model deployment, model management, and output consumption.
• Represent the DataRobot product to various personas across the customer organizations spanning from data scientists to C-level executives.
• Influence our customers through thought leadership by using value-based messaging to translate complex technical terms into business outcomes.
• Act as a trusted advisor on AI across the customer lifecycle.
• Act internally as the voice of the customer to the Product and Engineering, informing the direction of future product feature development.
• Present at conferences and/or help create other marketing content.

Senior Applied AI Expert

• Partner closely with prospective customers, subject matter experts, and pre-sales team members to turn ambiguous business problems into compelling, production‑inspired prototypes.
• Rapidly design and build enterprise-grade MVP (Minimum Viable Product) agentic AI applications, often within days—that clearly demonstrate how AI agents can transform real business processes.
• Showcase technical depth, strong UX/UI taste, and modern AI architecture, while remaining pragmatic and fast to deliver.

Lead Pre-Sales Applied AI Expert

Customer Success Manager

• Accelerate Onboarding & Initial Application Adoption: Guide customers through first-use milestones by enabling key personas, resolving blockers, and ensuring consumption of initial apps deployed during onboarding.
• Drive Ongoing Consumption: Monitor usage, identify underutilized apps / stalled users, and engage with customers to increase activation and business impact.
• Customer Health Monitoring: Actively track product usage, satisfaction, and success milestones to surface risk early and coordinate mitigation plans.
• Technical Advocacy & Solution Feedback: Act as the voice of the customer to *’s product and engineering teams, channeling technical requirements, gaps, and enhancement requests.
• Accelerate Initial Group Learning Adoption: Facilitate onboarding workshops and training sessions for multiple user groups, enabling key personas to reach first-use milestones and overcome common blockers.
• Technical Enablement & Training: Deliver targeted, scalable enablement sessions and create reusable knowledge-sharing materials designed for diverse audiences across accounts.
• Use Case Value Realization: Collaborate with Engagement Directors to ensure learning initiatives align with business goals and capture feedback and outcomes for executive reviews.

Technical Success Manager

Backend Engineer

• Design, implement, and maintain database infrastructure using StatefulSets, Operators, and Helm charts to ensure databases are reliable, self-healing, and scalable.
• Own the deployment lifecycle for database clusters by managing version control for Helm charts and configuration templates.
• Support and administer production database systems by proactively instrumenting and monitoring performance, security, and availability within the containerized environment.
• Perform zero-downtime upgrades and migrations for major and minor releases, developing and maintaining Helm hooks and custom scripts to automate complex stateful operations.
• Manage and optimize performance for backend data stores, ensuring data consistency and integrity across pod life cycles.
• Develop and maintain automated backup and recovery processes, specifically designed for containerized databases, including volume snapshots and off-cluster storage integrations.
• Resolve critical production issues related to container resource limits, network policies, storage classes, and database-specific tuning/configuration within a Kubernetes cluster.
• Partner with application teams to implement database changes, review migrations, and ensure efficient resource utilization in the shared Kubernetes infrastructure.
• Develop and maintain automation tools and scripts (Bash, Python) specifically focused on simplifying Kubernetes management tasks, such as provisioning users/secrets and monitoring cluster state.

Senior Database Engineer

Full-stack Engineer

• Build a system that ensures micro-services are secure, performant, reliable, and can go from idea to production in an hour.
• Build a system that continuously provides recommendations to right-size computing resources for Kubernetes to ensure efficient cloud spending for ourselves and our customers.
• Design and architect automated quality platforms to go from Enterprise-Grade releases from once-a-quarter to once-a-week to once-per-day to once-per-hour without sacrificing performance, security, or reliability
• Work with Product, Legal and Security to ensure the continuous delivery processes you build are compliant and secure
• Work with the team to ensure pipelines have clear playbooks and can operate 24/7 without you
• Work with a diverse group of architects and platform engineers across our R&D department to set continuous delivery and performance requirements for all production services
• Work with internal product managers to set roadmaps and define milestones to deliver innovative and simple solutions to our many teams’ continuous delivery and platform engineering issues

Staff Software Engineer

Security Engineer

• Lead Federal Security: Serve as a primary technical lead for the DataRobot Federal Group, driving the acquisition and maintenance of Authority to Operate (ATO) at FedRAMP High and DoD IL5 levels.
• Compliance Engineering: Translate complex federal controls (NIST 800-53) into actionable engineering requirements for commercial developers.
• Audit & Policy Management: Write and maintain security policies (SSPs) and procedures.
• Security Engineering & Automation: Automate Everything: Develop custom automation to manage security tooling and implement "Secure-by-Design" processes in the CI/CD pipeline using Python or Go.
• Container Security: Identify, design, and implement controls to safeguard our containerized production environments.
• Customer Engagement: Act as the external face of DataRobot Security. Work directly with customers' security teams to resolve concerns regarding CVE exposure and architecture.

Staff Product Security Engineer

Data Engineer

• Architect and deliver scalable, reliable data warehouses, analytics platforms, and integration solutions.
• Critical role in supporting our internal AI strategy.
• Partner with Product Manager, Analytics to shape our project roadmap and lead its implementation.
• Collaborate with and mentor cross-functional teams to design and execute sophisticated data software solutions that elevate business performance and align to coding standards and architecture.
• Develop, deploy, and support analytic data products, such as data marts, ETL jobs (extract/transform/load), functions (in Python/SQL/DBT) in a cloud data warehouse environment using Snowflake, Stitch/Fivetran/Airflow, AWS services (e.g., EC2, lambda, kinesis).
• Navigate various data sources and efficiently locate data in a complex data ecosystem.
• Work closely with data analysts, and data scientists to build models and metrics to support their analytics needs.
• Data modeling enhancements caused by upstream data changes.
• Instrument telemetry capture and data pipelines for various environments to provide product usage visibility.
• Maintain and support deployed ETL pipelines and ensure data quality.
• Develop monitoring and alerting systems to provide visibility into the health of data infrastructure, cloud applications, and data pipelines.
• Partner with the IT enterprise applications and engineering teams on integration efforts between systems that impact data & Analytics.
• Work with R&D to answer complex technical questions about product analytics and corresponding data structure.