Software Engineer - ML Infrastructure

About the role

ABOUT ARENA INTELLIGENCE

Arena is the platform for evaluating how AI models perform in the real world. Founded by researchers from UC Berkeley's SkyLab, we're on a mission to measure and advance the frontier of AI for real-world use, and to build the foundation for everyone to understand, shape, and benefit from it.

Tens of millions of people use Arena each month to evaluate how frontier systems handle the work they actually do. The preferences they share power the most transparent, rigorous, and human-centered evaluations in AI. Leading AI labs, enterprises, and independent researchers rely on our work and open datasets to understand how models behave in real workflows: agentic coding, creative generation, professional productivity, and beyond. We go beyond leaderboards and decompose what human experience reveals about AI, so models advance toward the work people actually do.

We're a team of researchers, academics, builders, and creatives from UC Berkeley, Google, Stanford, and DeepMind. We seek truth, move fast, and value craftsmanship, curiosity, and impact over hierarchy. We're building a company where thoughtful, curious people from all backgrounds can do their best work together, in an office culture that radiates excellence, energy, and focus.

ABOUT THE ROLE

Arena Intelligence is seeking an ML Infrastructure Software Engineer to lead the design and development of scalable, low-latency model inference infrastructure, high-performance post-training infrastructure for reinforcement learning (RL), distributed ML training job execution, and comprehensive model evaluation frameworks. In this role, you’ll architect systems that execute complex training, inference, and evaluation workloads at scale, powering the insights that help researchers and developers build the world’s most advanced AI models. Working in close partnership with ML researchers, your contributions will be foundational to surfacing trustworthy, transparent, and timely evaluation and training signals across the platform. This role is ideal for someone who thrives in fast-moving environments, cares deeply about performance and reliability, and wants to build systems that advance state-of-the-art AI capabilities.

YOU’LL

- Architect, scale, and optimize LLM post-training infrastructure tailored for reinforcement learning (RL) alongside with low-latency model inference services

- Design and maintain robust job execution systems for large-scale distributed ML training and fine-tuning workloads

- Build comprehensive, automated model evaluation frameworks to deliver rapid, reliable feedback signals to ML researchers

- Ensure high availability, low latency, and fault tolerance across GPU clusters and distributed event streams

- Collaborate cross-functionally with AI researchers, product leaders, and infrastructure teams to support evolving training and evaluation needs

YOU’LL HAVE

- 5+ years of experience in software engineering, with a focused background in ML infrastructure, distributed systems, or large-scale data platform engineering