About

My name is Daniel. I'm an engineer based in Vienna, working at the intersection of machine learning, numerical optimisation, and robotics.

I'm currently approaching robotics from the ML and RL side, not from mechanical engineering or classical control. Most technical writing in this space comes from the other direction. I find that gap interesting, and that's what shapes what I write about.

This blog is where I document the things I'm building, the papers I'm reading, the concepts I understand deeply enough to explain clearly.

What I Do

I help companies build and deploy ML-powered systems from early prototype to production and advise on the decisions that determine whether an ML project succeeds or fails before a line of code is written. Current engagements are in finance and industrial robotics.

What Sets Me Apart

I write the code and do the consulting.

I'm not handing you a strategy deck and disappearing. I'm hands-on with architecture, model development, and the reality of getting ML into production. If something breaks, I debug it.

I understand the full stack.

From mathematical optimisation to containerised cloud infrastructure to explaining tradeoffs to non-technical stakeholders. I've taught this material at university level and shipped it in production in the same week.

How I Can Help

If you're building AI-powered products

AI Strategy & Implementation

The actual integration of ML models and pipelines into products that work reliably. I've built CV systems, RL agents, and statistical models end-to-end, including the MLOps layer for monitoring, retraining, and drift detection.

Numerical Optimisation & Simulation

Planning, routing, scheduling, and resource allocation problems. Linear, integer, and mixed-integer programming. Simulation-based decision-making for logistics, production, and robotics.

End-to-End Deployment

From model to monitored production system. Private/hybrid cloud on Azure and GCP, HPC clusters, Docker, Kubernetes, CI/CD.

If you need technical leadership or advisory

Technical Due Diligence

Assessing ML systems, infrastructure quality, and technical teams. I can evaluate whether what you've been promised is what you'll get, or audit what you already have.

Technology Modernisation

Migrating legacy infrastructure without taking systems down. I've done this: redesigned architectures, rebuilt CI/CD pipelines, moved teams from manual deployments to automated, tested, reproducible workflows.

AI/ML Advisory

Proof-of-concept scoping, model benchmarking, data strategy, feature engineering. For organisations that need a senior technical opinion before committing to a direction.

Technical Capabilities

ML & AI: PyTorch, Computer Vision, Reinforcement Learning, statistical ML, MLOps (monitoring, retraining, drift detection)

Optimisation & Simulation: NumPy, SciPy, linear/integer/mixed-integer programming, simulation-based decision making

Cloud & Infrastructure: Azure, GCP, Docker, Kubernetes, OpenShift, Terraform, Ansible, Slurm (HPC)

DevOps & CI/CD: GitLab CI, Jenkins, GitHub Actions, Infrastructure as Code, automated testing

Languages: Python, C++

Let's Talk If…

  • You need ML that works in production, not just in a demo
  • You're building in robotics, industrial automation, simulation, or finance
  • You need someone who can both design the system and build it
  • You want honest technical advice, including when the answer is "you don't need ML for this"
  • You're evaluating an acquisition or vendor and need a technical second opinion

Not a Fit If…

  • You need pure frontend or fullstack web development
  • You want someone to follow a detailed spec without thinking
  • You expect a working system without realistic timelines or data