# Senior AI Scientist

**Company:** [Pigment](https://hotfix.jobs/companies/pigment)
**Location:** London, United Kingdom
**Role:** AI Research
**Experience:** 5+ years
**Skills:** Python, LangChain, LangGraph, TensorFlow, PyTorch, scikit-learn, Generative AI, Natural Language Processing, LLMs, Agentic Architectures, Model Fine-Tuning, Tapas, Tabert, Turl, Time Series Forecasting
**Posted:** 2024-04-12

> Researches and develops generative AI features, agentic architectures, and language-model solutions for Pigment’s planning platform. The role combines experimentation and benchmarking with production Python development and requires advanced academic training in computer science, mathematics, or a related field.

## Job Description

## Responsibilities
- Develop AI features built on Pigment’s computation engine, emphasizing determinism, auditability, and data visualization.
- Build AI features aligned with an ambitious product vision and real-world usage.
- Contribute to a production Python codebase and follow experiments through ideation to production.
- Conduct experiments and benchmark models and architectures.
- Research advancements in agentic architectures, natural language processing, and large language models, applying relevant techniques to improve product features.

## Requirements
- Master’s or Ph.D. in Computer Science, Mathematics, or a related field.
- Strong programming skills in Python and relevant machine-learning and generative-AI libraries, such as LangChain, LangGraph, TensorFlow, PyTorch, and scikit-learn.
- Hands-on experience evaluating pretrained models and fine-tuning or training models.
- Experience with recent developments in agentic architectures.
- Excellent problem-solving skills and ability to work independently.
- Ability to communicate complex mathematical and AI concepts clearly across research, engineering, and product audiences.

## Nice-to-haves
- Experience building and shipping complex products on large language models.
- Experience with LLM- or deep-learning-based tabular-data understanding, including spreadsheet interpretation, schema inference, or table representation learning; examples include TAPAS, TaBERT, TURL, and Omniparser-like approaches.
- Knowledge of state-of-the-art deep-learning forecasting models, such as TimesFM, Chronos, and TOTO.

## Compensation and Benefits
- Competitive compensation package.
- Stock options.
- Health insurance through Alan Blue, fully covered for the employee and family.
- Weekly lunch and flexible lunch vouchers through Swile.
- Egym Wellpass subscription for access to gyms, studios, and wellness spaces across France.
- Trust-based, flexible working hours.
- Annual company offsite.
- High-end equipment, based on stock and availability.
- Remote-friendly environment.

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