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

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.

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

You respect key deadlines and pass on the results of your work to your teammates in an appropriate condition.

Individual responsibility.

You recognise areas for growth and proactively learn new skills and knowledge for your current and prospective areas of responsibility.

Lifelong learning.

You can plan your work several weeks ahead and can juggle multiple projects at once. You know when to postpone a task to keep your workload manageable.

Vision & planning.

You cover every important aspect of your task leaving out no crucial detail.

Thoroughness.

You offer help if you have spare capacity. You take initiative and pitch your own projects to others.

Proactiveness and initiative.

You question every judgement, claim or number and can engage in a healthy debate with your teammates.

Critical thinking.

You can challenge existing ways, abandon well-trodden paths and embrace the new in the name of the great.

Dynamic, out-of-the-box mindset.

Personal qualities

Noeon Research is an ambitious deep-tech startup working on a novel natively agentic AI system with general capabilities. We are an international 30-people team with headquarters in Tokyo, Japan. We are looking for an aspiring professional in the early years of their career to join our RnD team.

We are developing Noeon – a novel natively agentic AI system with general capabilities based on carefully selected principles from mathematical category theory, knowledge representation, and computational linguistics. Our technology prioritises interpretability, revisability, and generality, allowing for safe and efficient adaptation to a changing world. We prioritise AI safety by focusing on interpretability in order to unlock capabilities safely.

Machine Learning / Natural Language Processing

Computer Science, Mathematics, Computational Linguistics

Fully remote from anywhere in the world. However, EMEA / APAC preferred for a better time zone overlap with the Tokyo HQ

Not required, but is available upon request

Depending on the candidate's skill level, our target salary range is 14-17M JPY per year for a full-time position and equity compensation (options).

Competitive Salary

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

Compensation of overseas health insurance and additional medical costs.

Medical Allowance

Support for English and Japanese language training.

Language Courses

Develop and implement solutions leveraging open-source large language models (LLMs) to address specific business needs.

Fine-tune, customize, and optimize open-source LLMs for various applications, ensuring high accuracy and efficiency.

Evaluate and compare different open-source LLM architectures to select the most suitable models for given tasks.

Integrate LLMs into existing systems, ensuring seamless functionality and performance.

Stay updated with advancements in the field of natural language processing (NLP) and open-source LLMs, incorporating new methods and tools as appropriate.

Collaborate with cross-functional teams to define product requirements and deliver solutions aligned with organizational goals.

Responsibilities and Expectations

Proven experience in working with large language models (e.g., GPT, LLaMA, T5, BERT).

[essential]

At least 2 years of experience in Machine Learning or Natural Language Processing (NLP).

Hands-on experience in fine-tuning and deploying language models for specific tasks.

Designing and deploying RAG-enhanced solutions for enterprise use cases.

[required]

Open-source projects in the AI/NLP domain.

[advantageous]

Track record of papers accepted for publication in peer-reviewed international conferences and academic journals.

Experience

Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch).

Experience with NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK).

Knowledge of cloud platforms and MLOps (e.g., AWS, GCP, Azure, or Kubernetes).

Ability to integrate language models with external data sources, APIs, and knowledge graphs.

[advantageous] 

Experience in implementing Retrieval-Augmented Generation (RAG) workflows.

Familiarity with vector databases (e.g., Pinecone, Weaviate, FAISS) and retrieval systems.

Ability to design pipelines for document retrieval, indexing, and real-time query response.

Familiarity with optimization techniques for LLMs (e.g., pruning, quantization, knowledge distillation).

Experience in combining neural models with symbolic reasoning systems or rule-based approaches.

Understanding of ontologies, graph-based representations, and symbolic logic frameworks.

Understanding of distributed computing and GPU/TPU acceleration.

Technical skills

Degrees in Computer Science, Mathematics, Computational Linguistics, or a related technical discipline.

Educational background

Proactive mindset to stay updated with the latest advancements in AI, NLP, and neuro-symbolic systems.

Fluent in conversational and written business English (C1+).

Ability to work collaboratively in cross-functional teams.

Experience working using Agile framework.

Soft skills

Experience in companies using AI technologies in their work or developing their own solutions in this area.

AI Experience.

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

Startup Experience. 

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.

Full-Time Job

Depending on the candidate's skill level, we are willing to offer 13-21M JPY and equity compensation (options).

Outside scheduled meetings, you are free to work on your tasks whenever convenient.

Hybrid Work

Formulation of new contracts and finalisation of old contracts.

Finalising company policies and internal documents.

Creation and exaction of legal documents and processes within the company: restructuring, option programmes, set-up.

Communicating with lawyers and transactional communications. 

Create legal corporate structures for the company to optimise liabilities/taxes/opportunities: e.g. open up 3 more legal entities while structuring the IP well.

Create a more advanced internal legal framework and manage it: get company policies and WorkRules in order.

Create interesting and original legal solutions for corporate governance / ownership of the company, e.g. Shareholders agreements.

Registering patents and protecting the company's intellectual rights.

Communicating and working with government actors and regulators: compliance & obtaining grants and preferences.

What we need help with

10Y+ year experience as a Corporate Lawyer / Legal Officer.

In-house Counsel Experience (at least 3+ years).

Ability to solve interesting problems, not the boring corporate stuff of other companies.

 You will be a good fit if you have

Commitment

All-Round

Startup Mindset

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