Founding Engineers – Federated Learning for Anti-Money Laundering (AML)

Location: Copenhagen, Denmark (hybrid preferred) Remote in EU possible

Start: As soon as possible (applications reviewed continuously)

Domain: Federated Learning · Graph ML · Privacy-Preserving ML · RegTech / AML

About us

At Node16, we are building the first federated, privacy-preserving intelligence network that enables banks to collaborate against financial crime — without ever sharing raw data. Our mission is to create a trusted, cooperative data layer where financial institutions can jointly detect and prevent money laundering while respecting data sovereignty and privacy regulations. We’re a small, research-driven team at the intersection of machine learning, privacy technologies, and financial compliance, partnering with European banks to build a federated Data Commons for cooperative AML — one that eliminates centralized attack vectors while maintaining transparency and auditability.

If you’re excited by the idea of applying advanced ML to make a measurable impact on society — and want to help build a company from the ground up — this role might be for you.

As a Founding Engineer, you’ll be one of the first members of our engineering team — helping to build the first version of our federated learning platform while shaping our technical direction and culture. If you’re looking for technical decision-making responsibility, real impact, and broad ownership, this is it. You’ll work on a bit of everything, but some of the things you might expect to do include:

• Design and implement federated learning pipelines using NVIDIA FLARE

• Build and train ML models for typology detection and AML alert triage

• Set up and operate LLMOps and MLOps workflows

• Establish best practices in our codebase and development processes

• Design and

• deployment pipelines and secure hybrid infrastructure (on-prem + cloud)

• Help define our privacy architecture, including PETs such as HMAC pseudonyms, differential privacy, and TEEs, ensuring no raw PII ever leaves the originating bank

• Implement validation, explainability, and federated unlearning mechanisms to ensure compliance, traceability, and model integrity across banks.

• Contribute to the design of our Data Commons architecture, ensuring data sovereignty and resilience against centralized failure or attack points.

Our Tech stack:

Python · PyTorch · NVIDIA FLARE · Graph ML (PyG/DGL) · LLMOps · Kubernetes · Docker · CI/CD · PETs (DP, TEE, MPC) · Federated Unlearning / Model Auditability

You will also help define and evolve our stack — we value pragmatism over perfection.

Requirements

• Significant hands-on experience with ML engineering or distributed systems (typically 3–5+ years)

• Strong Python and PyTorch (or TensorFlow) experience

• Familiarity with federated learning and/or privacy-enhancing technologies (e.g., DP, TEE, encrypted gradients)

• Comfortable working end-to-end: data → model → deployment

• Excellent communication skills and ability to thrive in a small, fast-moving team.

• EU work permit

Nice to have

Experience in RegTech/FinCrime, security-minded engineering, or agentic/LLM workflows.

Why join

• Impact: Help reduce financial crime and define the future of cooperative AML in Europe.

• Ownership: Work directly with founders and bank partners on end-to-end solutions.

• Growth: Take on technical leadership early in a rapidly growing startup.

• Culture: Flexible remote setup with regular in-person collaboration.

• Rewards: Competitive early-stage salary and meaningful equity.

Hiring process

1. 20-minute intro call – background, motivation, and fit with Node16.

2. 45-minute cultural & experience interview – deep dive into your background.

3. Take-home task (~3h) – practical challenge.

4. 60-minute technical interview – review of task and further technical discussion.

5. Founder chat → references → offer.

All candidates who complete the take-home task receive feedback.

Apply now!

Send an application with

• CV + LinkedIn + (optional) GitHub

• A few lines on why Node16 excites you

• 1–2 examples of relevant work (repo, paper, demo, blog)