Staff Software Engineer - Machine Learning

Summary

Join Celonis, a global leader in Process Mining technology, as a Staff Data & Machine Learning Engineer. You will be responsible for improving and extending the Task Mining ETL pipeline and building AI-powered features. This role requires expertise in data engineering, software development, and machine learning using Python. You will design, build, and maintain data pipelines, develop AI features, implement data strategies, and collaborate with other engineering teams. The position demands strong technical leadership and mentorship skills. Celonis offers a competitive compensation and benefits package, including investment in personal growth and skill development, and a supportive work environment.

Requirements

• 8+ years of practical experience in a Computer Science/Data Science related field
• Or PhD in Data Science/AI/ML area with 5+ years of practical experience
• Experience with building production ready and scalable AI/ML applications in the python ecosystem
• Ability to optimize data pipelines, applications, and machine learning models for high performance and scalability
• Understanding of ETL jobs, data warehouses/lakes, data modeling, schema design
• Excellent command of SQL, including query optimization principles
• Ability to assess dependencies within complex systems, quickly transform your thoughts into an accessible prototype and efficiently explain it to diverse stakeholders
• Experience with containerization and CI/CD pipelines (e.g. Docker, Github Actions)
• Interest in learning new technologies (e.g. PQL language and Object Centric Process Mining)
• Strong communication and collaboration skills (English is a must)
• Able to supervise and coach mid-level and senior colleagues

Responsibilities

• Design, build, and maintain robust, scalable data pipelines that facilitate the ingestion, processing, and transformation of large datasets
• Drive the development of AI-powered features and applications from scratch within the Task Mining product
• Implement data strategies and develop data models
• Collaborate with other engineering teams to implement, deploy, and monitor ML models in production, ensuring their performance and accuracy
• Leverage machine learning techniques to provide actionable insights and recommendations for process optimization
• Write performant, scalable and easy to understand SQL queries and optimize existing ones
• Learn PQL (Process Query Language – Celonis’ own language for analytical formulas and expressions) and use it to query data from our process mining engine
• Own the implementation of end to end solutions: leading the design, implementation, build and delivery to customers
• Provide technical leadership and mentorship to other engineers and team members
• Lead design discussions, code reviews, and technical planning sessions to ensure high standards and knowledge sharing

Preferred Qualifications

• Knowledge of Column-oriented DBMS (e.g. Vertica) and its specific features would be beneficial
• Nice to have is knowledge in the frameworks Tensorflow, Pytorch, Langchain, FastAPI, SQLAlchemy

Benefits

• The unique opportunity to work with industry-leading process mining technology
• Investment in your personal growth and skill development (clear career paths, internal mobility opportunities, L&D platform, mentorships, and more)
• Great compensation and benefits packages (equity (restricted stock units), life insurance, time off, generous leave for new parents from day one, and more)
• Physical and mental well-being support (subsidized gym membership, access to counseling, virtual events on well-being topics, and more)
• A global and growing team of Celonauts from diverse backgrounds to learn from and work with
• An open-minded culture with innovative, autonomous teams
• Business Resource Groups to help you feel connected, valued and seen (Black@Celonis, Women@Celonis, Parents@Celonis, Pride@Celonis, Resilience@Celonis, and more)
• A clear set of company values that guide everything we do: Live for Customer Value, The Best Team Wins, We Own It, and Earth Is Our Future