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Responsibilities
- Training Infrastructure: Set up and maintain a reproducible ML environment across the compute spectrum, local development, GPU cloud (AWS), and HPC; ensure training is fast, consistent, and repeatable
- Model Training: Scale an existing training pipeline from research prototype to production, large labelled datasets, scoring across millions of properties
- Calibration: Implement and validate probability calibration, ensuring model outputs are statistically meaningful and externally defensible, not just good at ranking
- Experimentation: Build a rigorous experimentation framework with reproducible runs and clear data, feature, and model provenance; design validation strategies appropriate for geospatial data, and drive systematic hyperparameter optimisation and model selection
- ML/ModelOps: Manage experiment tracking, model versioning, and artifact lineage; maintain clean, reliable training and scoring pipelines for reproducible deployment
- Documentation: Produce model documentation that satisfies external technical review
- Communication: Ability to communicate results clearly with non-technical stakeholders
- Collaboration: Work closely with Data Engineers to build reliable, scalable training and scoring pipelines, and with Data Scientists to ensure features, labels, evaluation metrics, and calibration approaches are scientifically sound and production-ready
Requirements
Must haves
- Education: Master's degree or higher in computer science, machine learning, statistics, applied mathematics, or related quantitative field
- Experience: 5+ years of professional industry experience training ML models in production settings, with significant experience optimizing model performance for large-scale datasets, including training and inference (e.g., parallelization, distributed execution, or GPU acceleration)
- Calibration: Hands-on experience with probability calibration, you have debugged calibration curves and know when they break and why
- Evaluation: Strong grasp of evaluation for imbalanced classification: beyond accuracy, into calibration metrics and ranking quality
- Optimisation: Systematic hyperparameter optimisation at scale; experience with automated search frameworks
- ML/ModelOps: Experiment tracking, model registry, and artifact management in practice, not just in theory, including reproducibility, versioning, and reliable model deployment workflows
- Foundations: Strong Python, pandas / NumPy / scikit-learn; cloud compute experience (AWS) with GPU instances and distributed training or inference workloads
- Modern Tooling: Pragmatic use of AI tooling (Cursor, Claude, Copilot) as a core part of the development workflow
Nice to haves
- Experience shipping ML-powered features in a product development context (agile, CI/CD, production monitoring), not just research or offline analysis
- Spatial cross-validation, you know why random CV leaks in geospatial problems
- Uncertainty quantification: quantile regression, conformal prediction
- HPC experience (LUMI, SLURM-based clusters)
- Databricks ML Runtime, AWS RDS/Aurora, or PostGIS experience
- Insurance, catastrophe modeling, or climate risk vocabulary
- Tabular deep learning (TabNet, FT-Transformer) as comparison baselines
- Tech stack: Python, gradient boosting libraries, experiment tracking tooling, cloud compute (GPU)
Application Process
- Recruiter screening
- Hiring manager interview
- Technical task
- Technical panel interview
- Final interview
Working at ICEYEAt ICEYE, you’ll join a diverse and highly engaged team united by the ambition to make the impossible possible. As a global scale-up, we combine speed and ambition with the opportunity to take real ownership from day one. Your growth, wellbeing, and success are a priority, with continuous professional development, training opportunities, and a culture where collaboration is how we win.
How We Work (Our Values)Make the impossible possible: We set ambitious goals and stay calm under pressure. We bring grit, optimism, and ownership when things get hard, and we keep moving until we find a way.
- Be curious: Go deep, ask questions, listen carefully, and think critically. Understand the “why” behind decisions.
- See the big picture: Stay close to what’s happening across the company so you can make better decisions. Consider how your work affects others.
- Drive effective teamwork: Create psychological safety, invite different perspectives, and build inclusive teams. There are no bad questions.
- Act as one team: We win together. We match tasks to the right owner and stay agile as priorities shift.
- Have fun: What we do matters—and it should be enjoyable. Celebrate progress, take pride in results, and share the wins.
