Member of Technical Staff — Infrastructure

Remanence; Paris; Hybrid; EUR 110,000 - 150,000; Full time

Benefits offered by this job

Visa sponsorship

Relocation support

Flexible hybrid setup

Job summary

Remanence is building the next era of enterprise AI with scalable infrastructure for training and inference. You will own the compute and execution platform, enabling efficient use of GPU resources, reproducible experiments, and robust operation across large-scale ML workloads.

The role focuses on building and operating GPU clusters, sandboxed environments, and reliable data pipelines, with a strong emphasis on observability and performance improvements for researchers.

Qualifications

• Strong systems fundamentals and ability to reason about concurrency, resource contention and failure recovery.
• Experience with distributed ML workloads and scalable infrastructure.
• Familiarity with Linux-based environments, Docker and Kubernetes.
• Ability to build and operate data and artifact pipelines for ML experiments.

Responsibilities

• Build and operate GPU clusters, job scheduling, networking, and storage for distributed ML workloads.
• Develop execution platform for large numbers of concurrent environments with sandbox isolation and recovery.
• Build data pipelines for datasets, trajectories, model weights, and checkpoints.
• Own platform observability and reliability through capacity allocation and latency improvements.
• Provide researchers with reproducible environments and simple tools to launch and debug experiments.

Skills

Systems fundamentals

Concurrency

Resource management

Tools

Docker

Kubernetes

Slurm

Terraform

Python

Go

Job description

Member of Technical Staff — Infrastructure

Remanence is pioneering the next era of enterprise AI by building intelligent systems that learn continuously from real-world execution. We transform complex enterprise workflows and business context into dynamic, interactive environments where AI agents can safely learn, adapt, and improve. By combining high-fidelity simulation environments with state-of-the-art training loops, we build specialized models that solve long-horizon, complex tasks with unmatched reliability. We’re building the most talent-dense AI team in Europe to make this happen.

You’ll own the compute and execution platform supporting training, inference, task generation, and evaluation. Your work will make GPU resources productive and research workflows straightforward to operate and debug.

What you’ll work on

• Build and operate GPU clusters, job scheduling, networking, and storage for distributed ML workloads.

• Develop the execution platform for large numbers of concurrent environments, including sandbox isolation, resource limits, retries, and state recovery.

• Build reliable data and artifact pipelines for datasets, trajectories, model weights, and checkpoints.

• Own platform observability and recovery; improve capacity allocation, startup latency, and reliability through measurable changes.

• Give researchers reproducible environments and simple tools to launch, inspect, and debug experiments.

Relevant technologies

• Compute and orchestration: Linux, Docker, Kubernetes, Slurm, and Terraform.

• Execution and storage: Python or Go, Ray, S3-compatible object storage, and PostgreSQL.

• Observability: Prometheus, Grafana, OpenTelemetry, and NVIDIA DCGM; familiarity with GPU networking and NCCL is mandatory.

About you

You have strong systems fundamentals and can reason carefully about concurrency, resource contention, and failure recovery. You enjoy making complex infrastructure understandable and dependable for the people using it.

We welcome infrastructure specialists and exceptionally fast-learning generalists. Prior ML infrastructure experience is valuable; evidence of building and operating reliable systems matters.

What we offer

• Competitive compensation and equity.

• A fast-paced environment combining frontier research with impactful real-world applications.

• Visa sponsorship and relocation support for candidates joining us in Paris or London.

• A flexible hybrid setup, with a preference for working together in person.