Research Engineer: Scalable ML for Robotic Data (Toulouse)

TechTree; Toulouse; Hybrid; EUR 60,000 - 90,000; Full time

Benefits offered by this job

Equity: 0.1% - 0.15% stock options

Compute budget and conference travel

Location: Toulouse, France (on-site)

Job summary

TechTree is opening a research lab in Toulouse, France, to advance data-driven robotics and AI. You’ll join a team building the lab’s infrastructure, enabling researchers to run large-scale experiments with multi-GPU training, data pipelines, and real-world deployment capabilities in a fast-paced, globally distributed environment.

You will work on training stacks, data generation, reproducibility, and tools that accelerate research and bring results to production.

Qualifications

• Engineering degree or Master’s from a top programme; PhD a plus, not required.
• 2+ years as a research/ML engineer implementing papers in a team.
• Fluent in English (written and spoken).
• Experience training large models and handling distributed training, checkpoints and evaluation.
• Able to turn papers into runnable code and spot implausible results.
• Expertise in at least one of: distributed training/inference optimization; simulation; robotics software; large-scale video/3D data.

Responsibilities

• Build and run the Lab’s training stack: multi-GPU/multi-node training, experiment tracking, evaluation harnesses and compute budget.
• Turn raw multi-camera data into clean, versioned datasets and generate simulation data as needed.
• Reproduce papers quickly to validate ideas on our data within days.
• Design end-to-end experiments with researchers and identify when results are misleading.
• Deploy models to teleoperation lab and capture hardware, enabling practical tests.

Skills

Python

PyTorch

English fluency

Experimentation

Education

Engineering degree or Master's

Tools

Linux

Slurm

Kubernetes

Docker

CUDA

JAX

Job description

TechTree is opening a research lab in Toulouse, France, to advance data-driven robotics and AI. You’ll join a team building the lab’s infrastructure, enabling researchers to run large-scale experiments with multi-GPU training, data pipelines, and real-world deployment capabilities in a fast-paced, globally distributed environment.

You will work on training stacks, data generation, reproducibility, and tools that accelerate research and bring results to production.