Machine Learning Engineer

About Neuralk

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, your mission is to help data scientists move faster and further by building the intelligence layer of our predictive agent.

You will contribute to the design of the Agent:

the iterative process between the agent and the Data Scientist,

the capabilities of the Agent,

the integration of expert knowledge into the Agent,

enabling fast comparison of modeling strategies and robust evaluation across datasets and use cases.

Typical problems you will work on include real-world use cases such as churn or risk prediction, designing logic that generalizes across datasets, or structuring workflows that balance speed and methodological rigor.

You will collaborate closely with our research and engineering teams to ensure that advanced ML capabilities translate into a smooth, efficient and reliable user experience.

In this role, you will contribute to making advanced ML usable and effective for data scientists. You will be responsible for:

• Predictive Pipeline Design: Build and structure end-to-end expert predictive pipelines (data → model → evaluation) optimized for specific use cases.
• Agent Intelligence: Implement the decision logic that powers the agent’s ability to guide, assist and automate parts of the data science workflow.
• Workflow Abstractions: Translate complex ML processes into clear, reusable abstractions that scale across datasets and problems.
• Integration with Research Outputs: Leverage models, tasks and capabilities developed by the research team, without working on core model training or synthetic data generation.
• Product Collaboration: Work closely with product and engineering to ensure ML workflows are intuitive, reliable and aligned with user needs.
• Code Quality & Reliability: Contribute clean, modular and well-tested ML code to a shared codebase used in production.

• M.S. in Computer Science, Machine Learning, or a related field.
• 7+ years of experience in Machine Learning Engineering or applied ML roles.
• Strong hands-on experience building end-to-end ML pipelinesused by real users.
• Proven expertise in tabular machine learning, including problem scoping, feature engineering, modeling, and evaluation strategies.
• Solid ML fundamentals with the ability to reason about modeling choices and trade-offs.
• Excellent Python skills and experience writing clean, modular, production-grade ML code.
• Comfortable working at the intersection of research outputs and product constraints.
• Autonomous, impact-driven, and comfortable in fast-moving startup environments.
• Fluent in English and able to collaborate across teams.

Expertise

• Advanced Data Science: Strong experience designing, evaluating and iterating on predictive models for real-world use cases.
• ML Workflow Design: Proven ability to structure end-to-end data science workflows.
• Agent-like Systems: Experience building systems that guide, automate or assist on  workflows (e.g. assistants, intelligent pipelines, decision logic).
• Tabular ML: Deep understanding of structured data, feature engineering, validation strategies and common pitfalls.
• Product-Oriented ML: Ability to translate ML complexity into usable abstractions for other data scientists.

• Salary €80-100k/year
• Equity (BSPCE), to reflect the value you bring to Neuralk and foster a shared journey
• Comprehensive health insurance
• 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
• And more to come as we grow