ML Engineer

The Role

We are developing a hyper-personalised learning tool for adults. It’s not a chatbot but language is a critical consideration. Your work would lie at the intersection of knowledge representation, resource recommendation, LLMs, neural IR, and pedagogy. This R&D position focuses on new solutions rather than optimisation. You’ll have a lot of freedom in how you work; you will be told the problems to solve rather than the approaches to take. This is a hybrid office position requiring you to be in London regularly, if not daily. The work is hard but rewarding.

The Company

Grasp is an edtech research lab, re-imagining how humans master complex subjects by themselves. We currently provide products to a select group of partners in academia and industry.

Out team is intentionally small and multi-disciplinary, focused on design, pedagogy, and engineering. We have investment from world-class investors and are actively hiring. We have a firm belief that meritocracy, integrity, and hard work are prerequisites to success. We are based at Somerset House, London. Grasp is also a member of Makerversity, a pioneering community of over 350 world-leading entrepreneurs, creators and innovators.

Requirements

Mandatory

• STEM MSc or higher.
• 5+ years experience in Machine Learning, in particular Natural Language Processing (PhD counts).
• Recent, intensive experience in one (or more) of the following: GNNs, recommender systems, neural IR, knowledge distillation, semantic networks.
• You are well versed in how to leverage large language models in your area of expertise.
• A track record of framing problems, prototyping solutions, and integrating them into larger systems.
• Professional – timely, honest, team-player etc.

Desirable

• STEM PhD.
• Research publications or presentations.
• Startup experience.
• Long term member of a research community (meetup/signal/telegram groups etc.) in relevant/adjacent fields.

Signs that you might be the right person

• You enjoy working on hard problems.
• You can learn new areas quickly and thoroughly.
• People tell you that you can explain difficult things in a simple way.
• You think you have a good sense of what’s “good enough”.
• You have a reputation for getting stuff done.
• You’re a curious person.

Benefits

• Sign-on stock options bonus designed for the long term.
• A* colleagues with backgrounds at top firms.
• A mission you care about.
• In contact with reality (everything is linked to the user).
• Nice office environment.
• Great technology/kit budget.