Expert Knowledge Representation: Founding Engineer

About Atman Labs
At Atman Labs we are building software to emulate human expertise. We believe our research poses a credible path to emulate true human cognition and interaction with deep knowledge and proactive reasoning, which has largely been impossible to do via standalone Artificial Intelligence techniques. Our unique research is inspired by biological priors, lies at the intersection of custom Reinforcement Learning environments and Large-Scale Knowledge Representation, and is evolved and compounded with commercial application. As an applied research and commercialization company, we are deploying our platform in products across a number of commercial domains to demonstrate the value of our approach – starting with building proactive shopping concierges for e-commerce, to eventually launching expert systems across travel, healthcare, education, science and more.

The Next Frontier of Knowledge Representation: Industry-Leading Multi-Modal Knowledge Graphs as the Foundation of Intelligent Systems that can Reason
We are hiring for a founding engineer that will join the team responsible for building specialized systems that emulate how human experts acquire, represent, and recall knowledge. Human experts have spent thousands of hours reading and looking at documents, implicitly forming extensive knowledge graphs in their brains which connect the various concepts they learn about. Every new document they read or interaction they have with the world also updates their mental model of associations between concepts – and in turn their underlying graph – in real time. When presented with a goal, experts explore this graph to trace connections between relevant concepts, reasoning about their knowledge to form and dynamically adapt their strategies until they reach their goal.

For any domain of expertise, we start by building a world model that supersedes that of even the most experienced human expert. As foundation models for text and images mature, it becomes easier and cheaper to embed documents in high-dimensional spaces across text, images, and video to extract semantic information like a human would. Fusing these signals from disparate web-scale sources and across media types in a graph-like structure, and then effectively representing this knowledge to be reasoned on by the rest of our system towards a goal, pose the most exciting challenges and intersect knowledge graphs with reinforcement learning, computer vision, and NLP. By solving these challenges, you will be advancing state of the art in multi-modal knowledge representation and its use in the next generation of intelligent systems.

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. Our founding team prides itself in building a highly conscious culture that intentionally uplifts each member towards their personal ambitions while fostering collective innovation. To contribute to and grow alongside our team, you should either have the following qualities, or be willing to rise to them with our support:

Technical

• You have a PhD or equivalent industrial expertise in the application of knowledge representation and web-scale search with knowledge of the limitations and frontiers of the field.
• You have deep expertise in Computer Vision or Natural Language Processing, and have industry experience working with transformers, as it pertains to forming knowledge graphs across petabytes of text and video from the internet. You are curious about how these knowledge graphs can eventually be generated in one pass from any document.
• You have experience with ontologies, and understand how the frontier of unsupervised entity extraction, disambiguation, and relation extraction across text, images, and video can unlock self-organizing and updating ontologies.
• You are familiar with hierarchical clustering algorithms for community detection, event detection, topic modeling and the statistical tools available to make sense of large amounts of data to build structure and classification on top of knowledge graphs from documents.
• You have worked with or are excited to explore the frontier of knowledge graph embedding models and Graph Neural Networks (GNNs), to represent highly complex knowledge graphs in a lower-dimensional state for systems to reason on them. You recognize the challenge of ensuring the performance of these models on a graph that is updated dynamically and regularly.
• You are familiar with semantic information retrieval and search techniques for knowledge graphs, which combine both graph and vector databases and can generate and query documents and embeddings at scale, as well as how these generalize across text, images and video and their intersection, for example text-image joint embeddings. You are curious about multi-modal RAG (MuRAG) techniques and can assess limitations and tradeoffs.
• You can formulate methods to collect web-scale data, and are meticulous to understand the nuances of data from a human expert perspective to identify errors and inconsistencies.
• You have 7+ years of programming experience in Python and have development experience with both DL 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.

Personal

• 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, and are able to educate audiences at all levels on the novel applications and relevance of reinforcement learning.
• You are eager to amplify a team that has deep research and product experience, coupled with a bold vision for the future of intelligent systems.
• You can easily distinguish authentic and high integrity thinkers from ‘posers’, while also critically evaluating 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 to 1000 when you become authentically interested in a topic.
• You not only have interests in reinforcement learning, but are deeply curious about a range of interdisciplinary topics, ranging from knowledge graphs, recommendations, web-scale search, deep learning, generative AI models, computer vision and the opportunity to build truly intelligent systems in software that are inspired by biology.
• You can show high creativity and intensity in your personal pursuits, and your intelligence, creativity, and motivation is not limited to only one discipline.
• You consider yourself an innovator and an 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. Don’t worry, 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 the intelligence of human experts in machines, but we believe that this mission can also allow us to conceive many commercial products that yield billions of dollars of commercial revenues to support an ambitious R&D effort for years to come. We are building for a future where humans coexist alongside benevolent AGI and we will be at the forefront of executing on this vision. 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.

We’re excited to meet you. If you are too, send a short message with a list of your projects and highlights, as well as a brief paragraph of your life’s story, to [email protected].