Job Offer Physora - MLOps Engineer

Transvalor; Valbonne; Hybrid; EUR 52,000 - 65,000; Full time

Benefits offered by this job

Housing aid

Daily transport coverage

RTT (reduction of working time)

Occasional work from home

Job summary

Physora, a Transvalor Group company, is seeking an MLOps Engineer to oversee the full lifecycle of AI surrogate models for material-forming processes.

You will build scalable training infrastructure, enable industrialization, and ensure reproducibility with in-house datasets, model registry, and deployment on-premise or at the edge. Collaboration with R&D and production teams is essential to deliver reliable, high-performance solutions.

Qualifications

• Master’s degree or PhD in Computer Science (specialization in Machine Learning / AI) or Computational Physics with significant experience in distributed training systems engineering.
• Substantial production experience with training infrastructure; not purely academic.
• Deep expertise in PyTorch and its ecosystem, including distributed training and profiling.
• Comfort with large volumes of numerical data and storage formats (HDF5, Zarr, Parquet).
• Strong software engineering discipline: testing and version control.
• Curiosity about physics and willingness to understand complex material-forming processes.

Responsibilities

• Build and maintain training infrastructure: loading large-scale results, distributed multi-GPU training, experiment tracking, model registry, reproducibility.
• Develop tooling to transform raw simulation outputs into usable datasets with storage schemas and versioning.
• Achieve required inference performance: quantization, export, serving within plant latency budgets (on-premise/edge).
• Run experiments and ablations; present results to inform architectural decisions.
• Contribute to model implementation alongside the modeling engineer.
• Bring production discipline from outside and anticipate past failures.

Skills

Distributed training systems

PyTorch ecosystem

High-performance computing

Software testing

Education

Master’s/PhD in CS or Computational Physics

Tools

PyTorch

HDF5

Zarr

Parquet

MPI

C++

Job description

Job Offer Physora - MLOps Engineer

• Job Title: MLOps Engineer
• Company: Physora - A Transvalor Company
• Location: Sophia Antipolis
• Contract Type: Permanent (CDI)
• Status: Executive

Reporting to: Head of R&D

COMPANY

Physora is a young Transvalor Group company, founded in 2026 and based in brand-new offices at the heart of the Sophia Antipolis technology park. It is dedicated to developing and industrializing AI surrogate models for the numerical simulation of material-forming processes, including forging, casting, welding and heat treatment.

These models can be integrated into the Group’s digital workflow—FORGE®, DIGIMU®, REM3D®, TRANSWELD®, THERCAST® and SIMHEAT®—to reduce cycle times from hours to seconds.

They will also enable us to expand into production and quality control, with real-time prediction solutions powered by high-quality simulations from the Group’s solvers.

POSITION

You will oversee the entire lifecycle of Physora’s models: from building the training infrastructure (scalable, reproducible and traceable) to high-performance industrialization (inference under plant constraints, on-premise and at the edge), including transforming raw data into usable datasets and optimizing architectures through rigorous experimentation, while bringing proven production discipline to avoid past pitfalls and ensure reliable, differentiated and deliverable solutions.

This is the role that determines the pace. It makes the difference between a team running three experiments a week and one running thirty — which, in this field, accounts for most of the gap between success and failure.

MISSIONS

• Build and maintain the training infrastructure: loading large-scale results, distributed multi-GPU training, experiment tracking, model registry, reproducibility.
• Develop the tooling that transforms raw simulation outputs into usable datasets, including storage organization, schemas and versioning.
• Bring models to the required inference performance level: quantization, export, serving within the plant’s latency budget, on-premise and at the edge.
• Run experiments and ablations, and present their results in a way that informs architectural decisions, rather than simply reporting numbers.
• Contribute to model implementation alongside the modeling engineer.
• Bring production discipline from outside. You will be the person who has already seen these things fail elsewhere, and you are expected to say so.

PROFILE

• Master’s degree or PhD in Computer Science (specialization in Machine Learning / AI) or Computational Physics, combined with significant professional experience in distributed training systems engineering (PyTorch, high-performance computing, large-scale data management).
• Substantial experience with training infrastructure in production, outside an academic context. For this role specifically, this is non-negotiable.
• Deep expertise in PyTorch and its ecosystem: distributed training, profiling, and the debugging experience that comes from having seen models fail silently.
• Comfortable working with large volumes of numerical data and the storage formats that make them usable — HDF5, Zarr, Parquet or equivalent.
• Strong software engineering discipline: testing, version control, code that others can read.
• Curiosity about physics is essential. You do not need to be an expert in forming processes, but you must be eager to understand, decipher and master them.

YOUR STRENGTHS

• HPC and SLURM environments; large-scale distributed training.
• Scientific computing (C++, Fortran, MPI).
• On-premise model serving and embedded inference; MLOps tooling.

POSITION DETAILS

• Location: Biot / Sophia Antipolis;
• Full-time permanent contract (CDI);
• Occasional work from home;
• Annual gross salary: between €52 and €65k;
• Executive status.

OUR BENEFITS

• An international, dynamic, and stimulating work environment;
• A positive atmosphere within teams passionate about digital technologies and scientific research;
• An attractive compensation and benefits package (aide au logement, prise en charge du transport quotidien, RTT, travail à domicile occasionnel).

More about Physora: www.physora.ai