{"id":96869,"url":"https://alion.io/job/arena-machine-learning-scientist-2","title":"Machine Learning Scientist - ML Research","company":{"id":1017,"name":"Arena","domain":"arena.ai","url":"https://alion.io/company/arena","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":83,"open_postings":13,"ghost_share":0,"stale_share":0.462,"repost_share":0,"time_to_fill_p50_days":73,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":203000,"max_usd":445000,"period":"year","method":"role_country_seniority_unknown","sample_n":4027},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Contrastive Learning","optional":false},{"name":"Diffusion Models","optional":false},{"name":"DPO","optional":false},{"name":"Edge AI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reward Modeling","optional":false},{"name":"RLHF","optional":false},{"name":"TensorFlow","optional":false},{"name":"Transformers","optional":false}],"status":"live","first_seen_at":"2025-12-18T08:49:07Z","employer_posted_date":"2025-12-18","last_verified_at":"2026-10-16T12:33:55Z","board_verified":true,"closed_at":null,"days_open":302,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":301},"description":"About Arena Intelligence\nArena 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.\nTens 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.\nWe'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.\nAbout the Role\nArena Intelligence is seeking a variety of machine learning scientists to help advance how we evaluate and understand AI models. You’ll help design and analyse experiments that uncover what makes models useful, trustworthy and capable through human preference signals. Your work will contribute to the scientific foundations of understanding AI at scale.\nThis role is deeply interdisciplinary. You’ll work closely with engineers, product teams, marketing and the broader research community to develop new methods for comparing models, analyzing preference data, and disentangling performance factors like style, reasoning, and robustness. Your work will inform both the public leaderboard and the tools we provide to model developers.\nIf you’re excited by open-ended questions, rigorous evaluation, and research that’s grounded in real-world impact, you’ll find a meaningful home here. We’re looking for:\nHands-on experience training large-scale models, including reward models, preference models, and fine-tuning LLMs with methods like RLHF, DPO, and contrastive learning.\n\nStrong foundation in ML and statistics, with a track record of designing novel training objectives, evaluation schemes, or statistical frameworks to improve model reliability and alignment.\n\nFluent in the full experimental stack, from dataset design and large-batch training to rigorous evaluation and ablation, with an eye for what scales to production.\n\nDeeply collaborative mindset, working closely with engineers to productionize research insights and iterating with product teams to align modeling goals with user needs.\n\nYou’ll\nDesign and conduct experiments to evaluate AI model behavior across reasoning, style, robustness, and user preference dimensions\n\nDevelop new metrics, methodologies, and evaluation protocols that go beyond traditional benchmarks\n\nAnalyze large-scale human voting and interaction data to uncover insights into model performance and user preferences\n\nCollaborate with engineers to implement and scale research findings into production systems\n\nPrototype and test research ideas rapidly, balancing rigor with iteration speed\n\nAuthor internal reports and external publications that contribute to the broader ML research community\n\nPartner with model providers to shape evaluation questions and support responsible model testing\n\nContribute to the scientific integrity and transparency of the Arena Intelligence leaderboard and tools\n\nYou’ll have\nPhD or equivalent research experience in Machine Learning, Natural Language Processing, Statistics, or a related field\n\nStrong understanding of LLMs and modern deep learning architectures (e.g., Transformers, diffusion models, reinforcement learning with human feedback) Proficiency in Python and ML research libraries such as PyTorch, JAX, or TensorFlow\n\nDemonstrated ability to design and analyze experiments with statistical rigor\n\nExperience publishing research or working on open-source projects in ML, NLP, or AI evaluation\n\nComfortable working with real-world usage data and designing metrics beyond standard benchmarks\n\nAbility to translate research questions into practical systems and collaborate across engineering and product teams\n\nPassion for open science, reproducibility, and community-driven research.\n\nWhat we offer\nWe 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.\n\nComprehensive health and wellness benefits, including medical, dental, vision, and additional support programs.\n\nThe opportunity to work on cutting-edge AI with a small, mission-driven team\n\nA culture that values transparency, trust, and community impact\n\nCome help build the space where anyone can explore and help shape the future of AI.\nArena 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. 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