We use a management approach that is close to Agile. Among others, we use guided continuous improvement, task prioritisation, sprints, value delivery, and focus on commitment.

Agile Experience.

Abstract thinking.

like Notion, Slack, Zotero, Shortcut, Miro, Git.

Infrastructure Tools

A strong advantage would be

The company develops a system capable of semantic processing of programming code, e.g., code analysis, transformation, and synthesis, as well as operating a computational environment. 

The approach combines explicit knowledge representation in discrete structures with ML-assisted algorithms to manipulate those structures. The system we design should be able to follow the reasoning trajectory and examine the internal logic; identify a lack of information and interact with external sources; incorporate new knowledge at later stages equally inexpensive as at the initial ones.


Machine Learning / Data Science / Software Engineering

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People tend to think that AI is solely Machine Learning, like ChatGPT (or DALL-E 2, or Microsoft Bing, or ChatSonic). We are building our solution on an alternative architecture which incorporates ML as a part.

Not just an ML

We put much effort into profound research and development of new underlying technologies rather than reusing existing ones. We like to explore unfairly neglected and non-conventional approaches to AI.

Going beyond

We want to be responsible in our research and development. We don't turn our noses up at AI safety research and take the issue of alignment seriously.

Safety and Alignment

That is, of course, a very basic condition. We want our colleagues to be healthy, happy and recreated.

Good Salary

Our team members work from both the office and homes all around the world. We are OK with that. However, we like to gather sometimes at the office to get on the same page.

Hybrid Remote

Outside scheduled team meetings, teammates are free to work on their tasks independently. When to work is a personal choice. Just do not overwork – that is inefficient.

Flexible Schedule

High autonomy is crucial for us – the small and agile team can achieve a breakthrough if its members are professional and independent. However, we promote helping each other. That is crucial too.

Autonomy

Automated code synthesis is a holy grail: many want it, but nobody has it. You can read more about it on the Company tab or just write to us. We love cross-pollination.

Challenging Work

What we value most are performance and results, not a strict process following. However, metrics, processes, and documentation are important too. We just keep their priorities low.

Low Bureaucracy

What we are offering

Running open-sourced models on our hardware.

Building pipeline for distributed training.

Integrating models with the rest of our systems.

Staying on top of architectures and frameworks.

Analysing and suggesting improvements for datasets.

What we need help with

In-depth knowledge of: Machine Learning, Deep Learning, Training customisation, ML frameworks.

Known architectures: Transformer, LLM, GNN, Autoencoders.

Bachelor’s degree or higher. English C1 or higher.

Who we are looking for

Make and fulfil commitments on time and at a high-quality level.

Commitment

Ability to figure out how to do new tasks from adjacent specialisations.

All-Round

Focus on the objective, looking for new approaches, fast forwarding orientation.

Startup Mindset

Presenting own arguments, listening to others' arguments, developing the next steps to resolve the difficulty.

Constructive Discussion

What soft skills are essential

It is handy while discussing low-level things. It happens.

Degree in CS.

Corporate experience differs from the startup’s one. It is useful to have aligned expectations.

Startup Experience. 

The approach combines explicit knowledge representation in discrete structures with ML-assisted algorithms to manipulate those structures. The system we design should be able to follow the reasoning trajectory and examine the internal logic; identify a lack of information and interact with external sources; incorporate new knowledge at later stages equally inexpensive as at the initial ones.

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Create full-stack web applications (internal tools for the research team).

Design interfaces between the backend and the rest of our pipeline.

Make seamless interoperation between the tools.

5+ year experience as a Software Engineer.

Deep knowledge of modern web UI frameworks: Vue or React.

Who are we looking for

Ability to work efficiently on the most prioritised tasks without external control.

Autonomous Work

will help colleagues to understand new concepts and plan their further work.

The ability to hold thought-leading presentations

Ability to switch quickly between different levels of abstraction and different contexts.

like Notion, Slack, Zotero, Shortcut, Miro.

Applied Math, Computer Science, Software Engineering

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Architecture. Designing system architecture that accounts for both theoretical developments and practical considerations.

Algorithms. Developing algorithms from theoretical descriptions and mathematics. Establishing exact training protocols for ML models and designing requirements for Engineers work.

Prototyping. Designing prototypes and proof-of-concept level solutions, that demonstrate core features of the company technology.

Assessment of feasibility. Serve as a constant communication channel between Research Scientists representing the theoretical side of things and Software/ML Engineers representing the practical side of things. Assess which proposed solutions and approaches are feasible from both sides' perspectives.

Maintaining systemic vision. Answering clarifying questions about all parts of the system, keeping knowledge about solutions up to date, identifying blind spots and problems with system design.

Balancing conventional and unconventional. Analysing and deciding which parts of the system should be designed and implemented using pre-existing algorithms and solutions and which require exploration. Similarly deciding on which problems should be solved by using Machine Learning and which by classical algorithms and data structures. Finding the optimal balance between interpretability, corrigibility, transparency and performance, feasibility, attainability.

5+ year experience as Research Engineer. Designing system architecture that accounts for both theoretical developments and practical considerations.

Experience in several of these areas: Program synthesis, Applied category theory, Knowledge Representation, Functional programming, Discrete Math, Applied Statistics, Algorithmic Complexity, Formal logic, Machine Learning, Transformers, GNN.

Languages: Python, Rust, C++, Haskell, OCaml, F#, Scala.

Master’s degree or higher. Also English C1 or higher