About Neuralk-AI

Neuralk is a deep-tech company building the next generation of Foundation Models for Data Science. Our mission is to build the predictive layer for businesses, transforming data science from a series of one-off initiatives — stitched together across silos, overly bespoke, and dependent on a handful of specialists — into a durable capability: a scalable predictive infrastructure that continuously learns from an organization's data and powers decisions across the enterprise.

As an early-stage, well-funded AI startup, Neuralk builds on state-of-the-art research to solve concrete business challenges. We value clarity over complexity, strong fundamentals over hype, and fast iteration grounded in rigorous engineering. Joining Neuralk means working hard in a fast-moving, research-driven environment, with a high level of ownership and the opportunity to shape a core product at the intersection of machine learning, engineering, and real-world impact.

About the Role

Scaling foundation models to structured data is not a solved problem. Unlike text or vision, tabular data has no canonical tokenization, no natural sequence, and no shared feature space across datasets, which means standard scaling laws, positional encodings, and pretraining objectives don't transfer. The architectural and optimization challenges are fundamentally different, and largely open.

At Neuralk, we're building the infrastructure and the models to solve this. As a Research Engineer, you'll work at the intersection of systems and research — owning the pretraining stack, pushing inference to production-grade latency, and writing the low-level GPU code that makes large-scale experimentation possible. The same people who build the systems shape the research direction.

Role & Responsibilities

• Transformer inference optimization: Design and implement optimizations to reduce latency and improve scalability of our foundation models in production: attention mechanisms, KV-cache, batching strategies, quantization.
• Pretraining optimization: Build and maintain the training infrastructure that enables us to scale pretraining efficiently (distributed training, mixed precision, gradient checkpointing, throughput optimization)
• CUDA kernel development: Write low-level GPU kernels to optimize critical bottlenecks, from custom attention variants to data preprocessing pipelines.
• Architecture experimentation: Collaborate with the research team to implement, train, and evaluate novel architectures adapted to structured data at scale.
• Tooling & infrastructure: Develop the internal tooling (experiment tracking, evaluation pipelines, benchmarking) that enables rapid and reproducible research iterations.
• Research contribution: Contribute to publications and engage with the broader ML research community.

Profile

• Master's or PhD in Computer Science, Machine Learning, or a related field
• 5+ years of experience in ML systems, with a strong focus on training and inference optimization
• Deep proficiency in Python and PyTorch, with strong software engineering practices
• Hands-on experience writing CUDA kernels or using low-level GPU optimization libraries (Triton, cuBLAS, CUTLASS)
• Solid understanding of transformer architectures, training dynamics, and scaling behavior
• Experience with distributed training frameworks (DeepSpeed, FSDP, or equivalent) and cluster environments (SLURM)
• Excellent communication skills in English
• Self-starter, autonomous, and comfortable in a fast-paced startup environment

Bonus points

• Publication record at top ML venues (NeurIPS, ICML, ICLR, MLSys)
• Experience with inference serving frameworks (vLLM, TensorRT, ONNX)
• Contributions to open-source ML infrastructure
• Experience with structured or tabular data

What We Offer

• A competitive salary
• Equity (BSPCE), to reflect the value you bring to Neuralk and to foster a shared journey
• Substantial compute resources and state-of-the-art ML infrastructure
• Comprehensive health insurance
• French level paid leave and time-off work
• Dynamic work setting. Although our preference is for in-person collaboration, we will be flexible with occasional remote work arrangements. Offices in Paris and London.
• and more to come as we grow