Job description

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 - Hands-on experience with PyTorch, model serving engines, and distributed training frameworks - Solid background in reinforcement learning (RL) post-training infrastructure, RLHF, or automated model evaluation pipelines - Proven ability to design and operate low-latency, high-throughput, and fault-tolerant distributed services at scale - Strong foundation in systems design, GPU cluster utilization, and performance tuning - Thrive in a fast-paced, high-ownership environment working closely with ML researchers WHAT WE OFFER - We offer competitive compensation aligned to the markets where our team members are based. The compensation range for each role is listed on its posting and will depend on the candidate's experience and work location. - Comprehensive health and wellness benefits, including medical, dental, vision, and additional support programs. - The opportunity to work on cutting-edge AI with a small, mission-driven team - A culture that values transparency, trust, and community impact Come help build the space where anyone can explore and help shape the future of AI. Arena Intelligence provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity, or gender expression. We are committed to a diverse and inclusive workforce and welcome people from all backgrounds, experiences, perspectives, and abilities.

Requirements

Must-haves

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
5+ years of experience in software engineering, with a focused background in ML infrastructure, distributed systems, or large-scale data platform engineering
Hands-on experience with PyTorch, model serving engines, and distributed training frameworks
Solid background in reinforcement learning (RL) post-training infrastructure, RLHF, or automated model evaluation pipelines
Proven ability to design and operate low-latency, high-throughput, and fault-tolerant distributed services at scale
Strong foundation in systems design, GPU cluster utilization, and performance tuning
Thrive in a fast-paced, high-ownership environment working closely with ML researchers

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

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Platform: ashby
Effort: quick

Benefits

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Dental

Vision

Wellness