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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