Applied Scientist, AI4Engineering

Jobtailor; Paris; Sur place; EUR 90 000 - 130 000; Plein temps

Résumé du poste

Jobtailor in Paris seeks a senior scientist specialized in physics-based simulations and machine learning to lead large-scale HPC campaigns and model development.

You will collaborate with automotive, aerospace, or semiconductor clients, applying OpenFOAM/ANSYS/COMSOL/Abaqus and building robust ML-enhanced workflows; expected to publish and contribute to open-source projects.

Qualifications

• PhD in physics or engineering; 5+ years in relevant domain.
• Fluent English with strong communication, able to explain complex concepts.
• Self-directed with ability to work without detailed roadmaps.
• Collaborative and eager to learn at the intersection of simulation and ML.
• Publications in engineering or ML venues are valued.

Responsabilités

• Design and run large-scale simulation campaigns using domain-specific solvers (e.g., OpenFOAM, ANSYS, COMSOL, Abaqus).
• Train AI models on physics data with rigorous evaluation of coverage, accuracy, and quality against industry validation standards.
• Build tools and frameworks for automated dataset creation, simulation pipeline management, and model evaluation.
• Collaborate with the science/research team on training runs and diagnose failure modes from data gaps or architecture limitations.
• Manage research projects and client communications with engineering teams.

Connaissances

English

Self-directed

Collaboration

ML for physics

Publications

Physics simulations

Formation

PhD in physics or engineering

Outils

OpenFOAM

ANSYS

COMSOL

Abaqus

Description du poste

Responsibilities

• Design and run large-scale simulation campaigns using domain-specific solvers (e.g., OpenFOAM, ANSYS, COMSOL, Abaqus).
• Train AI models on physics data with rigorous evaluation of coverage, accuracy, and quality against industry validation standards.
• Build tools and frameworks for automated dataset creation, simulation pipeline management, and model evaluation.
• Collaborate with the science/research team on training runs and diagnose failure modes arising from data gaps or architecture limitations.
• Manage research projects and client communications with engineering teams.

Requirements

• Fluent English with excellent communication skills — able to explain technical simulation concepts to both engineering and non-technical audiences.
• PhD in physics or engineering and 5+ years of industry experience in a relevant domain. You work in a key engineering industry: Automotive, Aerospace or Semiconductors and have an interest in machine learning.
• Self-directed — you don't need detailed roadmaps to make progress.
• Low-ego, collaborative, and eager to learn at the intersection of simulation and ML.
• Demonstrated success through industrial projects, academic work, or personal projects.
• Have a deep passion for machine learning.
• Experience with simulation solvers (OpenFOAM, ANSYS, COMSOL, Abaqus, or equivalent).
• Applied ML methods to simulation or surrogate modelling.
• Experience automating large-scale simulation campaigns on HPC clusters.
• Contributed to a large open-source or industry codebase.
• Publications in engineering or ML venues (NeurIPS, ICLR, etc.).
• Love improving existing code by fixing typing issues, adding tests, and improving CI pipelines.