About
Matt Wilson
Senior ML Engineer & AI Systems Consultant
I'm a senior machine learning engineer and independent consultant with deep experience building ML systems that go to production and stay there. My work spans the full ML lifecycle — from framing the problem and designing the data pipeline, through training and evaluation, to deployment, monitoring, and ongoing improvement.
I've worked across consumer platforms and venture-backed startups at various stages. The problems differ, but the pattern is consistent: organizations that struggle with ML usually don't have a data problem. They have a systems problem.
As a consultant, I embed directly with your team to accelerate what you're already building — or to build the thing that's been too complex or too risky to tackle internally. I work in short, focused cycles with clear deliverables at every checkpoint. I have no interest in billable hours for their own sake.
I operate through Mean Delta Consulting LLC, taking on a small number of clients at a time to keep the work quality high.
Principles
How I approach every engagement.
Speed with safety
Fast iteration matters, but not at the cost of correctness, reliability, or the ability to understand what the system is doing. Every shortcut gets documented or removed before handoff.
Measurable results
If success can't be measured, the project isn't scoped correctly. I define metrics upfront and every engagement ends with evidence — not just working code.
Maintainable systems
Production systems will be modified by people who weren't in the design meetings. I build for the engineer on call at 2am, not just for today's happy path.
Documentation and handoff
Every engagement includes documentation proportional to its complexity. The client should be able to operate, debug, and extend the system without me.
Toolbox
Tools I reach for regularly. I'm framework-agnostic — the right tool depends on the problem.
Languages
- Python
- TypeScript
- SQL
ML & AI
- Deep learning frameworks
- Classical ML libraries
- LLM APIs and agent frameworks
- Embedding and vector search
Infrastructure
- AWS and GCP
- Containerization and orchestration
- Distributed compute
- Workflow orchestration
MLOps & Data
- Experiment tracking
- Model registries and CI/CD
- Relational and NoSQL databases
- Data warehouses
Let's build something together.
If you're working on a hard ML problem, I'd like to hear about it.