About this role
Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .
Reddit is continuing to grow our teams with the best talent. This role is completely remote friendly within the United States. If you happen to live close to one of our physical office locations (San Francisco, Los Angeles, New York City & Chicago) our doors are open for you to come into the office as often as you'd like.
The AI Engineering team at Reddit is building our own Reddit-native foundational Large Language Models (LLMs). This team sits at the intersection of applied research and massive-scale infrastructure, training models that truly understand the unique culture, language, and structure of Reddit communities. You'll join a team of distinguished engineers and researchers building the "engine room" of Reddit's AI future — the foundational models that power Safety & Moderation, Search, Ads, and the next generation of consumer products.
As a Staff Research Engineer for Post-Training & Evaluation Science , you will own the science of our model development "feedback loop." While pre-training builds the base models, you define how we measure whether those models are safe, smart, and "Reddit-native," and you set the post-training methodology that turns base checkpoints into high-performing endpoints. You will define the Reddit Benchmark — our internal standard for rigorous model quality across both generation and representation — and own the evaluation science that the rest of the org's iteration depends on.
Responsibilities
• Define the "Reddit Benchmark" evaluation standard: Own the methodology — not just the harness — for rigorously measuring model quality across Safety, Reasoning, representation/retrieval, and Reddit-specific knowledge. Decide what "Reddit-native" means in measurable terms and set the bar the org trains against.
• Own evaluation reliability and statistical rigor: Establish the science behind trustworthy evals — judge variance, multi-sample scoring, inter-rater/inter-sample agreement, sampling and temperature effects, and calibration of automated judges. You are accountable for whether a benchmark delta is real or noise. Drive the practice of evaluation as a release gate — offline against frozen datasets, and pre-merge in CI/CD — so regressions are caught before endpoints ship.
• Design model-as-a-judge methodology: Own judge selection, prompt design, calibration, and reliability for automated evaluation using frontier external models, enabling rapid, trustworthy iteration cycles.
• Set post-training recipes and strategy: Design SFT recipes (data mixtures,