Machine Learning Engineer

Neuralk; Paris; Hybrid; EUR 110,000 - 140,000; Full time

Benefits offered by this job

Equity (BSPCE)

Paid leave (France)

Remote-friendly setup

Job summary

Neuralk, based in Paris, is seeking a Senior Machine Learning Engineer to design the ML intelligence behind our Data Science Agent. You will embed data science methodology into practical, scalable workflows for data scientists, working with research and software engineers.

Join a fast-moving startup building Foundation Models for data science. You will shape modeling logic, evaluation strategies, and production-ready pipelines, with ownership and a strong focus on real-world impact.

Qualifications

• M.S. or PhD in Machine Learning, Computer Science, or Statistics.
• 7+ years of experience in applied machine learning or ML engineering.
• Strong hands-on experience building predictive models on tabular data.
• Deep understanding of model evaluation, validation and experimentation practices.
• Experience building ML pipelines or tools used by data scientists.
• Strong Python skills and production-quality ML code.
• Ability to reason about modeling trade-offs, benchmarking, and experimental design.
• Comfortable working at the intersection of research, product, and engineering.
• Autonomous and impact-driven in a fast-moving startup environments.

Responsibilities

• Design and implement the ML intelligence layer of the agent.
• Agent Modeling Intelligence: design and enable exploring modeling strategies, experiments, and assisting data scientists in building predictive models.
• Predictive Pipeline Design: build end-to-end predictive pipelines (data → features → model → evaluation) for real-world tabular datasets.
• Model Strategy & Evaluation: define how models are selected, trained, compared, and evaluated with robust validation.
• ML Workflow Abstractions: translate complex ML processes into clear, reusable abstractions for scale.
• Integration with Research Outputs: leverage research models and integrate them into usable workflows within the agent.
• Product Collaboration: work with product and engineering teams to ensure usefulness and reliability for data scientists.
• Code Quality: write clean, modular production-grade ML code.

Skills

Python

Tabular ML

Predictive Modeling

ML Pipelines

Experimentation

Model Evaluation

Product‑oriented ML

Startup mindset

Education

M.S. or PhD in Machine Learning, Computer Science, or Statistics

Tools

Python

ML Pipelines

Job description

Neuralk-AI is looking for a Senior Machine Learning Engineer to help design and build the machine learning intelligence behind our Data Science Agent.

Our agent is designed to help data scientists explore modeling strategies, build predictive pipelines, and evaluate models faster and more reliably. Your role will be to embed strong data science methodology and modeling expertise directly into the system.

This role sits at the intersection of applied machine learning, product, and engineering. You will work on the modeling logic, evaluation strategies, and predictive workflows that power the agent’s capabilities.

You will not work on core model research or infrastructure operations, but on translating advanced modeling capabilities into practical, reliable workflows used by data scientists.

You will work closely with our research team, who develop the core models, and with software engineers, who build the product platform.

You will be located in our Paris offices.

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.

Our product is a Data Science agent, powered by our Foundation Models, that assists data scientists throughout their workflow, from problem framing to robust, production-ready models. We focus on the hardest and most common data problems in companies: structured datasets describing customers, operations, risks or financial activity.

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. Our ambition is to redefine how predictive AI is built and used in organizations, at scale.

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.

Mission Highlights

As a Senior Machine Learning Engineer, you will help design the decision-making and modeling logic that powers our predictive agent.

Your mission is to encode expert data science practices into the agent so that it can guide users through modeling, experimentation, and evaluation in a reliable and scalable way.

You will work on problems such as:

• designing the interaction loop between the agent and the data scientist
• enabling the agent to explore and compare modeling strategies
• structuring predictive pipelines and evaluation protocols
• embedding expert knowledge about tabular machine learning into automated workflows

Typical use cases include real-world predictive problems such as churn prediction, risk modeling, or demand forecasting, where the challenge is to design workflows that balance speed, robustness, and methodological rigor across datasets.

You will collaborate closely with both researchers and engineers to ensure that advanced ML capabilities translate into a practical and powerful tool for data scientists.

Role & Responsibilities

In this role, you will contribute to the design and implementation of the ML intelligence layer of the agent.

• Agent Modeling Intelligence: Design and implement the decision logic that allows the agent to explore modeling strategies, run experiments, and assist data scientists in building predictive models.
• Predictive Pipeline Design: Build and structure end-to-end predictive pipelines (data → features → model → evaluation) optimized for real-world tabular datasets.
• Model Strategy & Evaluation: Define how the agent selects, trains, compares, and evaluates models, ensuring robust evaluation and sound methodological practices.
• ML Workflow Abstractions: Translate complex ML processes into clear, reusable abstractions that scale across datasets and predictive tasks.
• Integration with Research Outputs: Leverage models and capabilities developed by the research team and integrate them into usable modeling workflows within the agent.
• Product Collaboration: Work closely with product and engineering teams to ensure the agent’s capabilities are useful, reliable, and intuitive for data scientists.
• Code Quality: Write clean, modular, and well-tested ML code used in production systems.

Profile

• M.S. or PhD in Machine Learning, Computer Science, Statistics, or a related field.
• 7+ years of experience in applied machine learning or machine learning engineering.
• Strong hands-on experience building predictive models on tabular data.
• Deep understanding of model evaluation, validation strategies, and experimentation practices.
• Experience building ML pipelines or tools used by other data scientists.
• Strong Python skills and experience writing production-quality ML code.
• Ability to reason about modeling trade-offs, benchmarking, and experimental design.
• Comfortable working at the intersection of research, product, and engineering.
• Autonomous and impact-driven, comfortable working in fast-moving startup environments.

Expertise

• Tabular Machine Learning: Strong experience working with structured data problems, including feature engineering, modeling strategies, and validation pitfalls.
• Applied Predictive Modeling: Experience designing and evaluating predictive models for real‑world use cases.
• ML Workflow Design: Ability to structure robust and reproducible modeling pipelines.
• Agent‑like Systems: Experience building systems that assist or automate parts of the data science workflow.
• Product‑Oriented Machine Learning: Ability to translate complex modeling processes into usable tools for data scientists.

We are a fast‑paced startup, yet we value a healthy work‑life balance and thoughtful compensation. We offer:

• Equity (BSPCE), to reflect the value you bring to Neuralk and foster a shared journey
• French‑level paid leave and time‑off
• A dynamic work setting. Although our preference is for in‑person collaboration, we are flexible with occasional remote work arrangements