Offer summary

Qualifications:

Experience with transformer models and Natural Language Processing., Proficiency in implementing, training, and fine-tuning deep learning models., Familiarity with deploying ML models at scale and using ML frameworks., Knowledge of state-of-the-art deep learning and large language modeling techniques..

Key responsabilities:

Develop model evaluation frameworks based on specific task requirements.

Implement training and inference pipelines for model integration.

Integrate models as components of the production system.

Conduct research and document findings and designs to enhance system capabilities.

Job description

We are building an AI system for analysts and scientists, based on a fundamentally new approach to reasoning and knowledge representation. We go beyond state-of-the-art LLMs by combining algorithms in symbolic ways, to provide novel capabilities like performing multi-step analysis, displaying a verifiable reasoning path, and assessing uncertainty. We envision applications supporting and automating analysis and research in domains such as Finance, Strategy Consulting, Engineering, Material Sciences, and more.

We are looking for software engineers experienced with transformer models to contribute to the development of our inference pipeline. Tasks will focus on integration of pre-trained transformer models with symbolic reasoning.

Our team works fully remotely, and mostly within the CET timezone.

Useful experience

Work within Natural Language Processing

Implementation, training and fine-tuning of deep learning models

Deployment of ML models at scale (Serving models, Optimisation)

Experience with ML frameworks

Following state of the art deep learning and large language modelling

Responsibilities

Develop model evaluation frameworks based on task requirements

Implement training and inference pipelines

Implement models as components of our production system

Conduct research, and document findings and designs

Push the capabilities of our system by introducing state of the art techniques

On our website you can find more about our team and work culture, as well as example tasks that share some insight into the type of things team members are working on.

What we do: https://planting.space/

Ways of work: https://planting.space/org/

Team culture and example tasks: https://planting.space/joinus/

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