Machine Learning Scientist - Open Source Lead
Leads open-source ML research by designing experiments, developing evaluation methodologies, analyzing preference data, and releasing datasets/code to advance AI model transparency. Requires PhD-level expertise in ML/LLMs and hands-on experience with RLHF/DPO fine-tuning.
About the job
Responsibilities
- Design and conduct experiments to evaluate AI model behavior across reasoning, style, robustness, and user preference dimensions
- Develop new metrics, methodologies, and evaluation protocols that go beyond traditional benchmarks
- Analyze large-scale human voting and interaction data to uncover insights into model performance and user preferences
- Communicate results with the broader research community via academic papers, educational content, conference talks
- Collaborate with engineers to implement and scale research findings into production systems
- Prototype and test research ideas rapidly, balancing rigor with iteration speed
- Partner with model providers to shape evaluation questions and support responsible model testing
- Contribute to the scientific integrity and transparency of the LMArena leaderboard and tools
Requirements
- Hands-on experience training large-scale models, including reward models, preference models, and fine-tuning LLMs with methods like RLHF, DPO, and contrastive learning
- Strong 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
- Fluent 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
- Deeply collaborative mindset, working closely with engineers to productionize research insights and iterating with product teams to align research with user needs
- Comfortable being a visible representative of Arena Intelligence, engaging openly with the research community, and building a strong personal brand to help shape AI research culture
- PhD or equivalent research experience in Machine Learning, Natural Language Processing, Statistics, or a related field
- Strong 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
- Demonstrated ability to design and analyze experiments with statistical rigor
- Experience publishing research or working on open-source projects in ML, NLP, or AI evaluation
- Comfortable working with real-world usage data and designing metrics beyond standard benchmarks
- Ability to translate research questions into practical systems and collaborate across engineering and product teams
- Passion for open science, reproducibility, and community-driven research
Nice-to-Haves
- Skilled at public speaking, writing, and presenting research work to diverse audiences
- Actively participates in conferences, panels, and online forums to foster relationships and thought leadership
- Builds trust through transparent communication and consistent community engagement
- Serves as a go-to contact for external researchers, journalists, and partners
Skills
PyTorch, JAX, TensorFlow, Python, LLMs, Transformers, RLHF, Dpo, Machine Learning, Statistics
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