AI4Science.Studio · by Hugo Penedones
AI where the physics matters.
Partnering with biotech, pharma, materials, engineering, and manufacturing teams to solve hard machine learning problems.
How it works ↓
Hi, I'm Hugo Penedones. I have 20 years of ML engineering at the frontier — in science and in production, at scale. I was an early member of the AlphaFold team at Google DeepMind, the system behind the 2024 Nobel Prize in Chemistry. Later, I co-founded and led engineering at Inductiva, a cloud-HPC platform for simulation engineers.
Where I can help.
Biotech, Pharma & Materials
Machine learning for molecules and materials — from biological sequences to property prediction and generative design.
DNA/RNA Sequence Models · Structure Prediction · Materials Property Prediction · Generative Models · Machine-Learned Force Fields / Interatomic Potentials
Engineering, Manufacturing & Robotics
Fast surrogate models, physics-informed machine learning, and reinforcement learning for control, optimization, and design under real-world constraints.
Physics-Informed ML · Neural Operators · Design Optimization · Control & RL · Sim-to-Real · Surrogate Modelling
Accepting project inquiries · Available from May 2026
A focused studio.
One project at a time.
When I take yours on, it gets my undivided focus — not a fractional slice split across a dozen accounts.
Direct ownership.
If I need to scale up, I recruit a small group of scientists and ML engineers — but the intellectual lead is always mine.
Results, not presence.
You pay for outcomes, not hours.
Premium model.
Elite ML work is not cheap. But a failed 18-month internal project costs far more. I de-risk your investment with milestones.
AI-native engineering.
Coding is no longer the bottleneck; deeply understanding the problem and its constraints is. My value lies in pruning the search space — providing the intuition to skip dead-ends while leveraging AI to execute at 10x speed.
Human at every step.
I use the most advanced tools available — but you work directly with me. Just honest conversations, genuine curiosity about your problem, and work that's actually enjoyable to do together.
Typical engagements.
Long enough to do serious work. Short enough to stay focused. Projects don't drag on indefinitely.
Each phase has its own budget and clear go/no-go decision. You invest incrementally as confidence grows — not all upfront.
Scoping
Define the problem precisely, agree on success metrics, identify risks, and establish what a good outcome looks like. This phase ends with a written specification — no ambiguity.
Deliverable: technical spec & risk assessmentProof of Concept / De-risking
A working system that validates the core technical hypothesis. Fast iteration, honest evaluation. If the approach doesn't work, we find out here — not months later.
Deliverable: validated PoC & performance reportProduction system
The PoC is hardened into a robust, deployable system — with performance pushed to its limits. Code quality, reliability, and handoff are taken seriously from day one.
Deliverable: production-ready system & documentationPublication
OptionalWhen the work sits close to the research frontier and you want to make it public, I can lead writing it up for peer-reviewed publication.
Deliverable: submitted manuscriptLet's talk about your project.
I'm selective about the projects I take on — I want to work on things that are genuinely hard and where ML can make a real difference. If that sounds like your situation, I'd love to hear from you.
Currently accepting inquiries for projects starting May 2026.
Connect on LinkedIn