Senior ML Infrastructure Engineer (GPU Compute & Distributed Training)

flexion • Switzerland

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AI Summary

Lead the design, optimization, and operation of Flexion’s cloud-based GPU clusters for training large-scale AI models. Collaborate with AI engineers to maximize hardware utilization, influence compute strategies, and drive engineering best practices. Shape the autonomy stack for humanoid robots with direct impact on product roadmap execution.

Key Highlights

Own end-to-end GPU infrastructure for distributed AI model training (billions of parameters)

Collaborate with AI engineers to optimize training workloads and hardware efficiency

Strategic compute planning, multi-cloud cost optimization, and system reliability improvements

Key Responsibilities

Architect, deploy, and continuously optimize cloud-based GPU clusters for AI training workloads

Select and implement frameworks/tooling for efficient cluster provisioning, job scheduling, and monitoring

Collaborate with AI engineers to profile and optimize training pipelines for speed and hardware utilization

Develop short/long-term GPU compute strategies, including multi-cloud cost optimization and capacity planning

Enhance engineering practices (testing, documentation, reliability) across infrastructure and ML teams

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Technical Skills Required

Distributed GPU Training (DDP, FSDP, NCCL) Cloud Platforms (AWS/GCP/Azure) Python

Benefits & Perks

Competitive Compensation

Relocation & permit sponsorship

Enhanced pension plan, health/dental/vision coverage, 401(k) with company contributions

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Nice to Have

Experience with PyTorch Profiler, Dynolog, or Nsight

High-performance file systems (e.g., Lustre)

Infrastructure-as-code (Terraform, Ansible)

Job Description

At Flexion, we are building the autonomy stack for humanoid robots. Our mission is to drive the transition from fragile prototypes to real-world deployments of humanoids. We were founded by leading scientists in robot reinforcement learning (ex-Nvidia, ex-ETH Zürich) and backed by leading international VC firms. In just months, we went from our first line of code to deploying real humanoid capabilities with our customers, leveraging simulation and reinforcement learning. Today, we are rapidly expanding the capabilities of our autonomy stack, our customer base, and our team.

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