Forward Deployed Engineer

Neuralk; Paris; Hybride; EUR 90 000 - 130 000; Plein temps

Avantages offerts par ce poste

Equity (BSPCE)

Paid leave & time-off

Flexible remote arrangements

Résumé du poste

Neuralk is seeking a Forward Deployed Engineer to deliver predictive ML solutions in client environments. You will scope problems, improve data quality, and drive production deployments with rigorous evaluation against baselines.

You will collaborate with data scientists and engineers, ensuring a durable, repeatable deployment that scales across industries and use cases.

Qualifications

• Hands-on ML engineer with 3–7 years of production experience.
• Experience delivering end-to-end ML projects in production.
• Strong Python engineering and data pipeline skills.
• Proficiency with SQL, Parquet, and modern cloud deployments.

Responsabilités

• Own end-to-end client missions from scoping to production.
• Translate business objectives into measurable prediction problems.
• Adapt data pipelines to client stacks and ensure data quality.
• Run rigorous evaluations and compare against baselines.
• Collaborate with data scientists and engineers across teams.
• Provide interpretable outputs and maintain production monitoring.

Connaissances

Python engineering

Tabular data pipelines

ML fundamentals

SQL

Parquet

DuckDB

Cloud deployments

Docker/Kubernetes basics

scikit‑learn

LightGBM/XGBoost

PyTorch/JAX

Outils

SQL

Parquet

DuckDB

AWS

GCP

Docker

Kubernetes

scikit‑learn

LightGBM

XGBoost

PyTorch

JAX

Description du poste

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.

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
• 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

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

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