Research Engineer: Graph Machine LearningAtman Labs, London
About Atman Labs
At Atman Labs we are building software to emulate proactive human expertise. Emulating human experts with deep knowledge and proactive assistance has largely been impossible to do via standalone Artificial Intelligence techniques. As an applied research and commercialization company we are deploying our products in a number of domains to demonstrate the value of our approach – from proactive shopping assistance, to personal teachers to healthcare concierges – and with this commercial focus advance our unique research that lies at the intersection of Reinforcement Learning rewards, Large Scale Knowledge Representation, and Predictive Models inspired by biological priors.
The Next Frontier of Machine Reasoning: Web-scale Knowledge Graph Exploration using Reinforcement Learning
Human experts can form and explore structured mental models in their heads to solve open-ended problems across different domains. Our research seeks to emulate this process through a novel combination of using reinforcement learning agents to perform exploration through a knowledge graph. Knowledge graphs allow us to represent structured information and the logical relations that govern it, unlocking the ability to build reinforcement learning strategies that can learn to solve complex, open-ended problems across web-scale and continuously-evolving domains.
You will be leading the research on knowledge representation and how it can serve to build AI systems capable of such complex reasoning. You will work on formulating research problems that explore how Reinforcement Learning algorithms can interact with large and complex knowledge graphs to reason over ambiguous tasks. To do this, you will develop knowledge graph machine learning techniques that will power several tools within our products. Knowledge graph representations (e.g. embeddings) are critical to representing web-scale, structured information in a compact format for a reinforcement learning agent, ensuring scalability. You will lead the efforts on training and validating graph embedding algorithms that capture multi-hop semantics within large web-scale knowledge graphs. Additionally, you will develop link prediction models that will enhance both the reasoning over the knowledge graph and recommendations.
About You
We are looking for ambitious and independent thinkers who have a deep desire to contribute and want to be part of the team that makes this a reality for humanity. In order to contribute, you should have all of these qualities:
- You have a PhD degree or equivalent industrial expertise in Graph Machine Learning and its applications.
- You have a deep understanding of the state-of-the-art in graph machine learning, with a focus on learning graph embeddings and link prediction problems.
- You have experience in training and tuning various graph ML algorithms including GNNs, Message Passing and Graph Transformers. Experience in building graph-based recommendation systems is a plus.
- You have 5+ years of programming experience in Python and have development experience with toolkits like PyTorch or Tensorflow and can deploy models with clean APIs. You are equally capable as a software engineer as you are in formulating novel research ideas and your code proves it.
Moreover, in order to deeply fit within our culture, you should embody the following:
- You are capable of reasoning from first-principles, where there is no trodden path, as well as critically evaluate when existing ideas are worth considering.
- You are articulate and can present your ideas in writing, in person and in small groups educating audiences at all levels on the application of generative models.
- You have a high ‘faker’ detector in others, and can critically evaluate truth from fiction in your own work.
- Your colleagues consider you a highly positive personality, you amplify the energy of others rather than dampen the mood.
- Your intensity goes from 0-1000 when you become authentically interested in a topic.
- You not only have interests in systems engineering but are deeply curious about a range of interdisciplinary topics ranging from computational creativity, knowledge graphs, recommendations, web scale search, deep learning, large language models, computer vision, human consciousness, and the opportunity to build truly intelligent systems in software that are inspired by biology.
- Outside work you can show high creativity and intensity in your pursuits, you cannot easily be characterized in one discipline.
- You consider yourself an innovator, and original thinker, not a follower. You are looking for a way to contribute to the world and want to join our team to do so.
You want to work in person in London. We’ll sponsor your visa.
We have the ambition to usher the world towards co-existing alongside Benevolent AGI.
Not only do we believe that our work is a credible approach to functionally emulate human reasoning but we believe that this mission can also allow us to conceive many commercial products that yield billions of dollars of commercial revenues that can support an ambitious R&D effort for years to come. We are building for a future where humans coexist alongside benevolent expert systems and seek to advance the field from the front. We are looking for ambitious and independent thinkers who have a deep desire to contribute and want to be part of the team that makes this a reality for humanity.
Apply with a short message and a list of your projects, your life story in 5 sentences, your favorite book or artist, and your resume to .
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