Machine Learning Engineer

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 a Machine Learning Engineer to help scale and strengthen the core infrastructure that powers real-world AI evaluation. You’ll play a foundational role in shaping how we build, deploy, and improve our model benchmarking systems, working across data pipelines, inference APIs, and new evaluation methodologies. This is an opportunity to apply your technical expertise to a platform trusted by millions, and to help define how cutting-edge AI is assessed in the wild. As one of the first ML engineers on the team, you’ll partner closely with researchers, engineers, and product leadership to turn new ideas into reliable systems. You’ll help us move fast while staying rigorous, improving reproducibility, scaling up to new modalities, and deepening our ability to understand and compare frontier models.   YOU’LL - Architect and build what will become our core modeling for data and evaluation products - Own the full stack data, model training, and eval pipelines - Help grow a culture of feedback and rapid product iteration as we build new features as a tight-nit team - Conduct research into state-of-the-art evaluation methods and contribute to the long-term vision for a centralized, scalable evaluation platform.   YOU’LL HAVE - Strong programming skills with the ability to work across the stack in a typical recommendation system or LLM stack - Experience in deep learning, language models or reward model training - Experience in working with LLM for fine tuning, prompt engineering, function calling etc - Self-motivated with a willingness to take ownership of tasks - A passion for shipping quality products - 4+ years of industry experience or relevant projects - Solid understanding of statistics, and various tools and methodologies for evaluating uncertainty in a way that is specific to the given product being shipped 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.