Member of Technical Staff - GPU Infrastructure

Methodfi; City of London; On-site; GBP 80,000 - 120,000; Full time

Job summary

A technology startup is looking for an experienced infrastructure engineer to design and operate large-scale GPU systems. Your role will involve optimizing performance and cluster management while collaborating with research teams. The ideal candidate has deep systems experience in distributed computing and a strong understanding of GPU frameworks. This position offers top-tier compensation and benefits, including paid parental leave and health insurance.

Benefits

Competitive salary and equity

Comprehensive health insurance

Paid parental leave

Paid time off

Daily meals provided

Qualifications

• Experience in high-performance or distributed computing environments required.
• Familiarity with containerization and orchestration tools needed.
• Strong understanding of modern observability stacks and performance profiling.

Responsibilities

• Design and operate large-scale GPU infrastructure.
• Develop high-performance scheduling and orchestration systems.
• Optimize cluster utilization while maintaining reliability.

Skills

Infrastructure engineering

High-performance computing

Distributed computing

GPU knowledge

CUDA

Kubernetes

Containerization

Tools

PyTorch

DeepSpeed

JAX

Slurm

Job description

Our Mission

Reflection’s mission is to build open superintelligence and make it accessible to all.

We’re developing open weight models for individuals, agents, enterprises, and even nation states. Our team of AI researchers and company builders come from DeepMind, OpenAI, Google Brain, Meta, Character.AI, Anthropic and beyond.

About the Role

• Design, build, and operate Reflection’s large-scale GPU infrastructure powering pre-training, post-training, and inference.

• Develop reliable, high-performance systems for scheduling, orchestration, and observability across thousands of GPUs.

• Optimize cluster utilization, throughput, and cost efficiency while maintaining reliability at scale.

• Build tools and automation for distributed training, inference, monitoring, and experiment management.

• Collaborate closely with research, training, and platform teams to accelerate development and enable large-scale training and inference.

• Push the limits of hardware, networking, and software to accelerate the path from idea to model.

About You

• Deep systems or infrastructure engineering experience in high-performance or distributed computing environments.

• Strong understanding of GPUs, CUDA, NCCL, and large-scale training and inference frameworks and libraries (PyTorch, DeepSpeed, JAX, Megatron‑LM, SGLang, vLLM, etc.).

• Hands‑on experience with containerization, orchestration, and cluster management (Kubernetes, Slurm, or similar).

• Familiar with modern observability stacks and performance profiling tools.

• High agency and the ability to thrive in a fast‑paced, high‑ownership startup environment.

• Excited to build from zero to one defining how frontier‑scale training/RL infrastructure is architected and operated.

• Motivated by enabling researchers and engineers to build the world’s most capable open‑weight AI systems.

What We Offer:

We believe that to build superintelligence that is truly open, you need to start at the foundation. Joining Reflection means building from the ground up as part of a small talent‑dense team. You will help define our future as a company, and help define the frontier of open foundational models.

We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported.

• Top‑tier compensation: Salary and equity structured to recognize and retain the best talent globally.

• Health & wellness: Comprehensive medical, dental, vision, life, and disability insurance.

• Life & family: Fully paid parental leave for all new parents, including adoptive and surrogate journeys. Financial support for family planning.

• Benefits & balance: paid time off when you need it, relocation support, and more perks that optimize your time.

• Opportunities to connect with teammates: lunch and dinner are provided daily. We have regular off‑sites and team celebrations.