Forward Deployed Engineer

Job description

Open

About the Job Offer

As a Forward Deployed Engineer at Neuralk, you deploy and prove the value of our foundation model in real enterprise environments — not in demo conditions, but in production, with messy data, against existing models that your clients have spent years building.

The problems are quantitative prediction problems: credit risk, churn, predictive maintenance, demand forecasting. The rigour expected is real ML rigour — proper evaluation frameworks, benchmarking, distribution shift analysis, calibration. This is not an agentic role; you are not configuring LLM pipelines or engineering prompts. You're doing the kind of applied ML that actually moves a business metric.

You're not there to hand off documentation. You work hands-on with the client's data science and engineering teams: scoping the right prediction problem, getting their data to the right quality, running the evaluations that prove our model beats their baseline, and turning a successful proof of concept into a system the client can own and operate in production.

You report to the CRO and work closely with our research and ML engineering teams. Your field observations feed directly into both the product roadmap and the model's evolution.

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.

Role & Responsibilities

Deploy & Prove Value

Own the end-to-end delivery of each client mission: from scoping to production

Translate business objectives into well-defined prediction problems with clear success metrics and data requirements

Adapt the data pipeline to each client's stack: data ingestion, feature engineering, model integration, API wiring, infrastructure setup (cloud, on-premise, hybrid)

Design and run rigorous evaluations: benchmark Neuralk's foundation model against the client's existing models and industry baselines, with proper data splits, OOD testing, and calibration analysis

Help clients understand and trust the model: uncertainty quantification, edge case analysis, interpretability artifacts (SHAP, feature importance)

Monitor model behavior in production: logging, alerting, drift detection, performance tracking — and translate degradations into concrete feedback for the research team

Client Partnership

Act as the primary technical point of contact throughout the implementation and post-deployment phase

Lead technical workshops with quants, data scientists, infrastructure engineers, and business stakeholders

Help clients become autonomous — document, explain, and transfer knowledge so the solution outlasts your presence

Research & Model Improvement

Serve as the primary interface between field reality and Neuralk's research and product teams

Bring back the domain intelligence generated by each engagement: the business context, edge cases, and failure modes you encounter directly inform how the model is adapted and extended to new verticals and use cases

Contribute to the repeatable deployment playbooks and evaluation frameworks that scale across the team

Profile

Background

Machine Learning Engineer, Data Scientist, or ML-focused technical consultant with 3–7 years of hands-on experience

Has led at least one ML project end-to-end in a production environment — from raw data to deployed model — with real business ownership

Technical Skills

Strong Python engineering: modular, production-quality code — not just notebooks

Deep experience with tabular data pipelines: feature engineering, data quality, schema evolution, leakage prevention, missing data handling

Solid ML fundamentals: model calibration, overfitting trade-offs, evaluation methodology, interpretability (SHAP, feature importance)

Proficiency in SQL, Parquet, DuckDB or equivalent; cloud deployments (AWS, GCP or equivalent); Docker / Kubernetes basics

Familiarity with scikit-learn, LightGBM, XGBoost; PyTorch / JAX is a plus

Client-Facing Skills

Genuinely energized by solving problems with clients — not just tolerating it

Able to lead technical conversations with both quants / data scientists and non-technical stakeholders

Knows how to push back constructively when a client's framing of the problem is wrong

Comfortable managing expectations when a model underperforms or timelines slip

Mindset

High autonomy: you make decisions and move forward without waiting for a playbook

Comfortable with ambiguity: early-stage startup, evolving product, fast iteration

Fluent in English (mandatory); French is a plus

Bonus Points

Experience in Finance, Energy, Insurance, or another data-intensive vertical where tabular ML is central

Open-source contributions (scikit-learn, pytorch-tabular, deeptab or equivalent)

MLOps basics: MLflow, monitoring, reproducibility pipelines

Experience working with international or distributed teams

Compensation & Benefits

We are a fast-pace startup, yet, we favor a good work-life balance and interesting compensations. We offer:

A competitive salary

Equity (BSPCE), to reflect the value you bring to Neuralk and to foster a shared journey

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.

and more to come as we grow

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