ML Platform Engineer @ Bleu Robotics

EducationPals; Paris; Hybrid; EUR 70,000 - 110,000; Full time

Job summary

Bleu Robotics seeks the first software/ML platform engineer in Paris to build and operate a cloud-based service for training and inference. You will own infrastructure, training pipelines, and inference workloads, while shaping the platform from the ground up.

You will collaborate with frontend and ML teams, ensuring security, cost control, and reliability. The role is on-site in Paris, with freedom to choose cloud strategies and web frameworks as the team evolves.

Qualifications

• Experience building and operating cloud-based ML training/inference services.
• Background as software or ML platform engineer relevant to cloud services.
• Experience deploying in cloud environments.

Responsibilities

• Own infrastructure, training, and inference services for the platform.
• Develop secure, scalable cloud-native components.
• Collaborate with frontend and ML teams to deliver end-to-end workflow.

Skills

Deep learning

Python

MLOps

Cloud

Job description

You will be the first engineer at Bleu Robotics with cloud expertise. The scope covers infrastructure, training and inference services, the web front end, security, and cost. The choice of cloud provider and web framework is open; you will make it with the team. The role is based in Paris and is on-site. We are equally interested in two kinds of background: software or ML platform engineer who has built and operated a cloud service that runs training or inference jobs on data uploaded by users (

Relevance is based on skills and concepts, not a guarantee that you qualify.

Covered skills: Deep learning Python MLOps Cloud

Skills to close: None listed

Build the skills for this job

Coming-soon courses matched to this posting: outline, waitlist, and skill path.

1. Constraint Systems as Coordination Mechanisms

Understand how explicit constraints enable distributed teams to build complex systems without constant communication.

2. Anatomy of a Constraint Package

Break down the components that turn domain knowledge into enforceable design rules.

Learn the process of characterizing systems and encoding findings into actionable constraints.

4. Tool Integration and Automated Validation

Design constraint systems that integrate into development workflows and catch violations early.

5. Constraint Patterns in Software and ML Systems

Apply PDK principles to API contracts, deployment platforms, and ML infrastructure.

6. Ecosystem Coordination and External Adoption

Enable external teams to use your platform without deep internal knowledge transfer.

7. Building Your Own Constraint System

Apply the framework to your own platform, API, or hardware-software boundary.

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