What the role involves
We’re looking for candidates with experience building reinforcement learning-based LLM training pipelines.
As part of our founding team you may
- Train reinforcement learning-based LLMs to solve tasks in the domain of materials science, chemical engineering, and engineering science
- Integrate simulation tools and real hardware to collect data
- Work with and source data from vendors
- Evaluate, test, and deploy models
Qualifications (none of these are hard requirements)
- Industry experience or projects working on hard problems in machine learning (with Python / PyTorch)
- Being scrappy (getting things done, over theoretical soundness)
- Publication record in venues like ICLR, NeurIPS, ICML, and others
- Good visual taste / an appreciation for aesthetics
- A reputation for having an “engineering mindset”
- Strong communication skills
- Experience working with various engineering simulation tools (e.g. CAE, EDA tools)
- Interest in manufacturing, engineering, EPC, semiconductors, materials science
What they ask for
About Outerport
Building out new LNG plants, HVAC systems, or semiconductor processes rely on hundreds of iterations of feasibility testing (through simulation or real-world lab tests) where different designs (combinations of equipment) and parameters are validated. The parameters are often locked in PDFs (datasheets, wiring diagrams, PFDs/P&IDs) which take thousands of hours to convert into CSVs / JSONs, and the simulators don't have an easy API to work with. Outerport bridges the gap by finding documents from PLMs, extracting structured data from drawings, building a knowledge graph over them, and building autonomous AI agents that can fully automate this R&D process by running simulations and performing design checks.
Full Outerport profile