Ontologic Intelligence
Founding Research Engineer, Model Architecture
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
About Ontologic Intelligence
Ontologic Intelligence is building persistent causal reasoning infrastructure: a layer between raw data and AI that represents cause and effect with uncertainty, provenance and support for dynamic, messy real-world systems. LLMs handle causal questions when the answer is in their training data but fall apart on novel or complex systems. We think they need a causal substrate, and no one has built a scalable one yet. That's what we're building.
We are now assembling a founding team. This role will lead a priority research track: decoder-only transformers in which learned causal variables or persistent causal state influence internal computation.
What you would work on
- Design and implement transformer variants with learned causal variables or latent causal state.
- Experiment with modulation of attention, MLP, residual, routing or expert pathways.
- Connect persistent external causal graphs or memory with internal model representations.
- Implement factual and counterfactual forward passes under controlled interventions.
- Train small decoder-only language models from scratch and modify open-weight models.
- Build benchmarks and ablations for causal generalisation, intervention consistency and counterfactual reasoning.
- Probe whether causal information is genuinely represented and used internally.
- Contribute to research and publish strong results.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Required experience
- Strong PyTorch model-engineering skills.
- Deep understanding of attention, residual streams, MLP blocks and autoregressive training.
- Experience modifying transformer internals, not only prompting or fine-tuning APIs.
- Experience training language models at research scale.
- Ability to design controlled experiments, baselines and meaningful ablations.
- Strong research judgement and disciplined engineering.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Particularly valuable
- Mechanistic interpretability, causal representation learning, world models, memory-augmented transformers, learned routing, mixture-of-experts, slot-based models, graph neural networks, FlashAttention, CUDA or Triton.
What this is
A founding role with substantial ownership over the architecture, experiments and research codebase. Equity-only until we raise funding (targeted late 2026 / early 2027; there is no salary before funding). At raise, this converts to a salaried role. If you have runway and want deep ownership of frontier work, let's talk.
Apply
Email z@ontologiclabs.com or apply through LinkedIn. Please send a repository, paper or project showing substantive transformer or language-model work. Briefly explain what you worked on, why you expected it to work and how you evaluated it.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location