Junior Data Science Engineer

Strong Python ecosystem knowledge with the ability to experiment, iterate quickly, and troubleshoot real-world ML workflows using libraries like pandas, NumPy, scikit-learn, and web server tools like FastAPI.
1-5 years of experience in data science, machine learning, or AI development (or a top-tier CS/DS graduate with strong fundamentals).
Highly coachable, adaptable, and eager to learn—we prioritize raw ability, curiosity, and work ethic over specific years of experience.
Experience with ML model development, deployment, and evaluation—you should be comfortable turning data into insights and working with predictive models.
Some familiarity with data engineering best practices, including working with structured/unstructured data, feature engineering, and optimizing ML pipelines.
Proficiency in writing efficient, maintainable, and well-structured code, with an emphasis on reusability, scalability, and production readiness.
Experience with software engineering best practices, including containerization (Docker), CI/CD automation, and cloud-based ML deployment.
Ability to implement logging, monitoring, and debugging strategies to ensure the reliability and performance of AI/ML applications in production.
Strong foundation in Computer Science fundamentals, including object-oriented design, data structures, and algorithmic problem-solving.

Develop reusable, production-ready assets that accelerate AI/ML adoption for customers—ranging from demo environments to deployable templates.
Prototype and experiment with AI/ML workflows using Python, pandas, and modern AI tooling, ensuring they are scalable and customer-ready.
Implement and refine engineering best practices to improve performance, scalability, and maintainability of AI/ML solutions.
Work within existing infrastructure to support scalable AI deployments, including CI/CD automation, API integrations, and containerized environments (Docker, Kubernetes).
Contribute to, create, and maintain automated tests for AI/ML workflows.
Collaborate cross-functionally with product, sales, and marketing teams to scale high-impact solutions.
Work on real-world deployment challenges, including monitoring, logging, and improving reliability in AI/ML workflows.
Support customer engagements by working directly with users to validate solutions, improve adoption, and ensure real-world impact.
Stay ahead of industry trends, continuously refining our approaches and advocating for best practices in AI/ML engineering.
Work closely with enablement teams to scale adoption of our solutions through documentation, content, and training materials.