Machine Learning Engineer (Applied ML, B2B SaaS)

Description

Design and deploy machine learning models for prediction and decision-making on domain-specific operational data

• Advance and extend scheduling and resource optimisation, including multi-objective optimisation, constraint handling, and stable re-planning
• Build end-to-end models that learn from real operational data and improve planning accuracy over time
• Own the full ML lifecycle: data analysis, feature engineering, model development, evaluation, and monitoring
• Translate business problems into formal models and measurable outcomes

Examples of What You’ll Build

• Capacity planning algorithms that account for skills, time buffers, cool-down periods, and space constraints
• Intelligent job-to-talent matching with dynamic weighting for in-progress vs. new projects
• Automated project creation from structured and unstructured operational inputs (e.g. PDFs, free text)

Qualifications

• Master’s or PhD in Computer Science, Mathematics, Physics or a related field
• Several years of experience in machine learning, particularly with tabular data and time series
• Strong knowledge of at least one deep learning framework (PyTorch is preferred)
• Experience with mathematical optimisation and/or constraint programming (e.g. OR-Tools, CP-SAT, Gurobi)
• Proficient in Python and the data science ecosystem (e.g. pandas, scikit-learn)
• Experience with ML experiment tracking and model deployment (e.g. MLflow, Docker, Kubernetes)
• A self-driven, solution-oriented mindset: you don’t just build models, you understand the business problem behind them
• Fuent in English

Nice to Have

Beyond our core AI, we are exploring additional AI capabilities that will flow into the product over time. Experience in this area is not required, but a plus:

• Experience with LLMs or agent-based systems (e.g. RAG, information extraction from unstructured data, API-based workflows)
• Creative freedom in a young, technically ambitious team
• Direct impact of your work on the product and our customers
• A modern tech stack and a culture that encourages experimentation
• A real-world, domain-rich problem space, not another generic SaaS
• End-to-end ownership of AI features, from data exploration to production deployment