Founding Research Engineer

Founding ML Engineer - Frontier AI startup - Paris - Up to €125k + equity

About

Time-series forecasting is where NLP was five years ago: dominated by bespoke, single-use models built from scratch for every new problem. The bet here is that the same paradigm shift is coming — and this team is building the foundation model to drive it.

This is a well-funded early-stage startup pre-training foundation models on multi-terabyte time-series datasets. The founders are ML PhDs with deep experience building forecasting infrastructure at hyperscale — the problem they're solving is one they've lived firsthand. The team is small, highly international, and technically serious.

The model is currently encoder-based transformers with Mamba variants under consideration — the field hasn't converged, which is part of the point. Training runs on a single node today; multi-node and multimodal inputs (text, images, news) are on the roadmap.

This is not a research role. It's the hire that makes experiments fast, the system reliable, and the architecture decisions sound.

What you'll do

• Build and own training infrastructure end-to-end: pipelines, GPU utilisation, iteration speed, and reproducibility
• Architect and train time-series foundation models on diverse multi-modal datasets
• Design reproducible experiments to test, compare, and combine ideas from the literature
• Build data exploration tooling to understand correlations, sparsity, and structure across sources
• Deploy models to production via the API and platform — including the gritty details when ONNX export or torch.compile breaks
• Iterate on model capabilities based on direct customer feedback
• Help shape the engineering and research culture as the team scales

What you'll need

• Strong understanding of architectural differences between encoder and decoder models, and what those mean for infrastructure decisions
• Breadth across model architectures — transformers, diffusion, Mamba and others
• Fluency in Python and PyTorch or JAX
• Systems-level thinking: reasons from first principles, not from defaults

Optional Bonus

• Experience with low-level systems programming: CUDA, Rust, or C++
• Multi-node distributed training experience
• Background in time-series data or real-time data streams