Machine Learning Engineer

Dockflow is building the world's first LogOps solution, and we're looking for an ML Engineer to make our platform intelligent.

We make logistics & supply chain people’s lives better by building the world’s first LogOps Platform

The SaaS application launched in 2021

Last year Dockflow tracked over $21B worth of cargo across 15,000 companies

Our customers are located all around the world and we’re constantly in a truly global setting

The HQ is in Antwerp, one of the world’s leading container ports

Our culture is driven by radical candor and enjoying the process of scaling a company

We’re building AI-powered exception prediction and transport plan optimization for maritime logistics. You’ll develop ML models that process vessel tracking, terminal data, and IoT sensor streams to predict delays, detect anomalies, and automate decision-making. This is a greenfield ML role – you’ll architect our ML infrastructure from the ground up as part of an 18-month R&D project.

Phase 1 (Months 1-3):

Learn our logistics domain and data landscape through pair programming with the tech lead

Build initial data pipelines for multi-source logistics data (AIS vessel tracking, terminal updates, sensor streams)

Prototype exception detection models on real shipment data

Phase 2 (Months 3-9):

Develop unified ML models for delay prediction, discharge timing, and transshipment monitoring

Implement continuous learning pipelines that adapt to evolving shipping patterns

Build explainability layers (SHAP-based) so logistics teams trust the predictions

Phase 3 (Months 9-18):

Scale ML infrastructure for real-time processing (sub-5-minute latency on complex scenarios)

Integrate models into production workflows with A/B testing frameworks

Drive technical decisions on model architecture and data strategy

3+ years building and deploying ML models in production

Strong Python skills and experience with ML frameworks (PyTorch, TensorFlow, scikit-learn)

Hands-on with data pipelines and stream processing in graph databases.

Comfortable with ambiguity – you’ll define the ML roadmap, not just execute it

• Bonus: NLP experience, time-series forecasting, or logistics/supply chain domain knowledge