Neuralk AI is a seed-stage startup developing Tabular Foundation Models (TFMs) which process structured data (rows/columns) to make predictions. They are hiring a Field Engineer to build demos, documentation, integrations, and drive product adoption with customers. The role requires experience in Python, data science fundamentals, and a passion for both building and business.
What they want, where you stand, and the exact résumé edits to qualify.
Biggest lever: Demonstrate practical experience with traditional data science models and tabular data prediction, perhaps by applying it to a personal project.
A starter prompt for Claude Code, what you'll need, and how to reach them.
You are a senior Field Engineer for a seed-stage startup building Tabular Foundation Models (TFMs). Your goal is to create a compelling demo that showcases the value of a TFM for structured data prediction. For this task, you will build a web application using Next.js 16 App Router, React 19, Tailwind v4, and a Python backend (Flask/FastAPI) to simulate a TFM. The demo should allow users to upload a small CSV file (e.g., a dataset for predicting house prices or customer churn) with numerical and categorical features. The backend will process this data, perform a simplified 'prediction' using a pre-trained (or mock-trained) tabular model, and return the results. The frontend should display the uploaded data, allow basic column selection for input/target, and visualize the predictions (e.g., showing original vs. predicted values for a regression task, or class probabilities for classification). Focus on a clear user flow: 1. Upload CSV, 2. Select target column, 3. Get predictions, 4. View results. Prioritize user experience and clear messaging about the TFM's capabilities. For the MVP, assume a pre-trained simple model (e.g., scikit-learn RandomForestRegressor/Classifier) is already available and integrated into the Python backend. Ensure the Python backend can accept tabular data, perform inference, and return JSON results to the Next.js frontend. Build/verify gate: A user can upload a CSV, trigger a prediction, and see results visualized in the browser.
Neuralk AI | Field Engineer | Full-time | Hybrid (Paris/London) We are a seed stage startup building Tabular Foundation Models (TFM). This is an emerging space with huge potential. LLM's like Claude take text as input and produce text. Model's like nano banana take text as input and produce images. Tabular Foundation Models take rows/columns as input, and produce predictions. The world is sitting on huge amounts of structured data that isn't being used to its potential. Making predictions is hard. Today, most folks are building, training, maintaining their own models. A bit like you'd need to have done back in 2019 with LLM's. We want to change that. We're looking for an early career Field Engineer to help build out demos, docs, resources, integrations as well as driving product adoption directly with customers. Someone who is passionate about building, has shipped real software (or predictions), has experience with Python and data science fundamentals. If you're looking for something a bit different than just "hands on keyboard" engineering, and enjoy business as much as tech, this could be for you. Feel free to reach out at aaron.stillwell@neuralk-ai.com
Build a small public project using a popular Python data science library (e.g., scikit-learn, Pandas) to perform predictions on a tabular dataset. Focus on the core steps: data loading, preprocessing, model training, evaluation, and exposing predictions via a simple API or web interface. Aim for a 2-week sprint to ship an MVP.
Standard for data science/ML tasks.
Operator's standard web stack.
Standard Python web frameworks.
Core Python data science libraries.
For deploying Next.js frontend.
New: potentially hosting and interacting with tabular models or datasets on Hugging Face - ~half day.
Learn it: Search getting-started ↗
Get set up: Create the account/instance, generate the API key or credentials, and add them to your project's environment variables.
Email aaron.stillwell@neuralk-ai.com
“I'm fascinated by the potential of Tabular Foundation Models and built a prototype demo showcasing data upload, feature selection, and prediction visualization for a TFM. This could serve as a starting point for the kind of demos you're looking for. Would you be open to a quick call to share it?”
Open the original ↗