Data Science Software Engineer, DataRobot

• We're looking for a data science software engineer who is ambitious and eager to learn, grow and challenge themselves with guidance from a mentor who thrives in a fast-paced, ambiguous environment who rolls up their sleeves to solve problems with code, enjoys shipping high quality software loved by users, and is eager to grow their skillset in AI/ML
• You thrive in a fast-paced, highly autonomous environment, enjoy solving complex problems, and want to build AI solutions that truly scale
• 4+ years of experience with python for backend services and/or data science workflows
• Proficiency in writing efficient, maintainable, and well-structured code, with an emphasis on reusability and scalability
• Experience with API development, preferably using FastAPI, Flask, or similar frameworks
• Experience with containerization technologies (Docker) and orchestration (Kubernetes)
• Familiarity with Infrastructure as Code tools (Terraform, CloudFormation, Pulumi)
• Experience implementing CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI)
• Understanding of data engineering principles, including ETL processes and data pipeline development
• Ability to work in an environment with varying workloads - comfortable with both focused project sprints and periods of more exploratory work
• Highly coachable and eager to learn—we prioritize raw ability, curiosity, and work ethic over specific years of experience
• Strong problem-solving skills and the ability to adapt to shifting priorities—we’re looking for someone who thrives in a fast-moving, high-growth, and high-autonomy environment

Desirable

• The Data Science Software Engineer role in the Customer Engineering team is an exciting opportunity to work at the intersection of AI/ML engineering, solution development, and go-to-market strategy
• We're looking for a data science software engineer who is ambitious and eager to learn, grow and challenge themselves with guidance from a mentor who thrives in a fast-paced, ambiguous environment who rolls up their sleeves to solve problems with code, enjoys shipping high quality software loved by users, and is eager to grow their skillset in AI/ML
• This means:
• Developing scalable, productionizable AI assets that customers can confidently integrate into their workflows
• Ensuring AI solutions are resilient, maintainable, and easy to deploy, minimizing operational friction
• Engaging with early-adopter customers and internal teams to refine and validate solutions
• Collaborating with product, sales, marketing, and enablement teams to drive solution awareness and usage at scale
• Ship fast, learn fast, and help customers see value fast
• Beyond developing and refining AI solutions, you will also help prioritize future investments by identifying high-impact opportunities and gathering customer feedback to refine existing assets
• The Data Science Software Engineer role is an exciting opportunity to work at the intersection of AI/ML engineering, solution development, and backend infrastructure
• We are looking for a highly adaptable engineer who thrives in a dynamic environment, has had some exposure to data science workflows, and is interested in learning more about data science and machine learning
• You will bridge the gap between our data science team and production systems, focusing on building the backend infrastructure that supports our data science initiatives within SAP environments
• Develop and maintain scalable, production-ready backend systems that support our data scientists' AI/ML workflows
• Build reusable infrastructure components that accelerate the integration of AI solutions into SAP environments
• Design and implement data pipelines that connect various data sources to our ML models
• Ensure AI solutions are resilient, maintainable, and easy to deploy, minimizing operational friction
• Collaborate with data scientists to understand their infrastructure needs and translate them into technical solutions
• Work on engineering challenges during high-intensity project phases, balanced with innovation and improvement work during quieter periods
• Implement and refine engineering best practices to improve performance, scalability, and maintainability of our AI/ML solutions
• Support and troubleshoot production systems to ensure reliability and optimal performance
• Design and implement CI/CD pipelines for automated testing, building, and deployment of ML models
• Containerize applications and manage infrastructure as code to ensure consistent environments
• Monitor system performance and implement logging strategies for ML applications