Key Skills

Job Description

Build the Causal Brain for Global Industrial Supply Chains We are building a cyber-physical system that brings provable causal reasoning to the world's most complex manufacturing and supply chain networks. Our platform learns causal structure across ERP, TMS, WMS, and MES systems, computes real-time multi-echelon safety envelopes, and executes bounded physical micro-interventions inside live factory operations without stopping a line, breaching an SLA, or violating regulatory validation. We are a pre-funding, equity-only startup with a strong founding team, advisory board, and early micro-pilot options. We have spent close to a year developing product strategy, running client product-market fit sessions, and refining our architecture. We are now building our engineering and AI/ML foundations—and we need exceptional interns to help us get there. We are looking for the best and brightest technical students in the Netherlands who want to work on hard, meaningful problems alongside a world-class team. 6 Internship Positions We are hiring for six distinct roles across two tracks: AI/ML track (supporting Elena, Head of AI/ML, Ex. Google / Microsoft engineer) and Engineering track (supporting the Head of Engineering / CTO) . AI/ML Track 1. Causal Discovery & Inference Research Intern Focus: NOTEARS-style DAG optimisation, interventional conditional independence testing, MEC collapse algorithms, PAG screening for latent confounders. Ideal background: MSc or PhD student in Machine Learning, Statistics, or Applied Mathematics with coursework in probabilistic graphical models, causal inference, or optimisation. You will: Implement and test causal discovery algorithms on synthetic and real-world datasets, help refine our continuous DAG solvers, and contribute to research that directly shapes our architecture. 2. ML Engineering Intern (Production ML Systems) Focus: Productionising causal DAG solvers, building training/inference pipelines, implementing monitoring and versioning for ML models in regulated environments. Ideal background: MSc student in Computer Science or Data Science with Python, PyTorch/JAX, and some MLOps exposure (Docker, CI/CD, model registries). You will: Turn research prototypes into reliable, monitored, versioned systems that can run in production. 3. Data Science Intern (Telemetry & Feature Engineering) Focus: Multi-echelon telemetry ingestion, temporal alignment of asynchronous ERP/TMS/WMS/MES streams, feature engineering for the unified state matrix. Ideal background: MSc student in Data Science with strong SQL, Python, and time-series experience. You will: Work with real enterprise data streams, build pipelines that synchronise millisecond-precision machine telemetry with day-latency carrier feeds, and engineer features that power our causal models. Engineering Track 4. Systems Engineering Intern (Distributed Systems & Event Streaming) Focus: Building and testing Kafka/MQTT/OPC UA ingestion pipelines, microservices architecture, API gateway development. Ideal background: MSc student in Computer Science, Distributed Systems, or Software Engineering with experience in event-driven architecture and containerisation. You will: Design and implement high-throughput, low-latency ingestion pipelines that form the nervous system of our platform. 5. Enterprise Integration Intern (ERP/TMS/WMS/MES Connectors) Focus: Building and testing connectors to SAP, Oracle, Blue Yonder, Manhattan Associates, Siemens Opcenter. Understanding EDI 214/315, API rate limits, and legacy system constraints. Ideal background: MSc student in Information Systems, Industrial Engineering, or Software Engineering with interest in enterprise software. You will: Build secure, auditable connectors that link our platform to the systems that run global supply chains. 6. DevOps / Platform Engineering Intern Focus: CI/CD pipelines, infrastructure-as-code, edge agent deployment for shop-floor control, multi-tenant SaaS architecture. Ideal background: MSc student with Kubernetes, Terraform, and cloud platform experience. You will: Build the deployment and operations foundation that lets our platform scale from pilot to production. What We Offer Real ownership: You will work on core components of a patent-pending platform, not side projects. Mentorship: Direct collaboration with Elena (Head of AI/ML) and the Head of Engineering / CTO. Flexible structure: Internships can be full-time or part-time, 3–9 months, aligned with your academic program. Academic credit: We support MSc thesis and graduation projects. Modest stipend: Where possible, we provide a small monthly stipend (€400–€600) to cover expenses. Equity for exceptional interns: Outstanding interns may receive an equity grant or convertible note. Path to full-time: Exceptional interns will be first in line for full-time roles once we raise funding. Location: Amsterdam HQ, with hybrid/remote flexibility. What We Look For Exceptional technical ability: You are among the best in your cohort. You learn fast, think deeply, and ship. Ownership mentality: You take initiative, ask questions, and drive projects to completion. Collaborative spirit: You communicate clearly and work well in a small, fast-moving team. Curiosity for hard problems: You are excited by causal inference, distributed systems, or industrial automation. EU/EEA student or Dutch study visa: You are enrolled in a Dutch university or have a residence permit allowing internship work. How to Apply Send your resume, GitHub/portfolio, and a brief note on which of the six positions interests you and why to [email protected]. We are reviewing applications on a rolling basis and will move fast for the right candidates. These internships are structured for academic credit and modest stipends. Exceptional interns may receive equity grants. We are a pre-funding startup; cash compensation is limited at this stage.

Core Responsibilities

Contribute to building Kanvaz AI’s causal reasoning platform for industrial supply chains by developing causal discovery algorithms, production ML systems, data pipelines, distributed ingestion services, enterprise connectors, or deployment infrastructure. Work on core platform components, collaborate with the AI/ML and engineering leads, and help prepare the system for pilot and production use.

Requirements

Applicants should be technically strong students enrolled in a Dutch university or hold a residence permit that allows internship work, with relevant experience depending on the chosen role. The posting seeks initiative, fast learning, clear communication, collaboration, and interest in causal inference, distributed systems, or industrial automation.

Benefits

• Academic Credit
• Mentorship
• Flexible Internship Structure
• Modest Stipend
• Potential Equity Grant or Convertible Note
• Potential Path to Full-Time Employment
• Hybrid or Remote Flexibility