Don't sit in meetings for a mission you don't care about. Build tools to learn faster at Grasp.
You will be one of the first ML Engineers in the company. You will need to be self-sufficient as, for example, we do not currently have a dedicated MLOps team.
Currently most of our problems are in natural language. We primarily work with self-hosted, fine-tuned, transformer-based encoder models like mpnet, roberta, etc. for problem spaces like classification, semantic equivalence, entity extraction etc. On the generative front, we still primarily use externally hosted models like GPT-4, though we have a business preference to self-host open source LLMs in the future.
You will be responsible for the ML-directed growth and sanitisation of our in-house knowledge graphs. You will also design and iterate on models which leverage knowledge graphs to solve product problems. Specifically, we want you to handle model design, training, and deployment, as an individual contributor.
The work is hard but rewarding. Please apply if you have a desire to get stuck-in building something incredibly useful. Please don't apply if you are looking for an easy ride.
This is a hybrid office position requiring you to be in London regularly.
The mission of Grasp is to increase the rate at which humans learn. A decade from now, Grasp will be able to take anyone from novice to mastery, in any field, ASAP. We will be the centre of learning.
We've raised $4M+ from top tier investors including Balderton, Point9, and Mozilla! Our founders are Ed Matthews and Jacob Sidorov, who built Revolut's trading desk together, and are both avid self-learners.
Grasp is also a member of Makerversity, a pioneering community of over 350 world-leading entrepreneurs, creators and innovators.