Staff Research Engineer, Post-training & Evaluation

Reddit is continuing to grow our teams with the best talent. This role is completely remote friendly within the United States.

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.

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.

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.
• 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.
• 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, curriculum, ablation strategy) that convert base models into helpful, well-aligned endpoints; partner with engineering to scale them.
• Evaluate base and CPT checkpoints, not just endpoints: Design checkpoint-selection methodology across CPT experiments and LR studies, so we pick the right base before committing post-training compute.
• Drive synthetic data generation strategy: Define and curate high-quality instruction and evaluation sets to improve generalization where human data is scarce.
• Partner with Safety Engineering: Translate high-level safety policy into concrete classification metrics, probe sets, and CI/CD unit tests , including precision/recall at threshold, label-noise handling, and false-positive taxonomy for abuse detection (HHV).
• Diagnose post-training instability: Dive into loss curves and eval logs to identify alignment tax and capability degradation, and recommend the fix.
• Lead research direction: Set technical direction for evaluation and post-training across the team, mentor engineers and scientists, and represent the work internally (and externally where appropriate).

Required Qualifications

• 6+ years of professional ML experience (or PhD + 4+) with a direct focus on LLM post-training and evaluation.
• PhD or MS in CS, ML, NLP, IR, or a related quantitative field , or equivalent industry research experience.
• Deep expertise in evaluation reliability: judge/sample variance, multi-sample scoring, calibration, statistical significance, and the failure modes of automated evaluation.
• Strong experience building custom, domain-specific evaluation harnesses (e.g., lm-eval-harness, Inspect AI, LightEval).
• Experience evaluating both generation and representation/classification: model-as-a-judge for generative quality and precision/recall, PR-AUC, retrieval/MTEB-style metrics, gold-label denoising, and label-noise handling.
• Deep understanding of Continuous Pre-training (CPT), Instruction Tuning (SFT), and how data quality shapes model behavior.
• Fluency in Python; strong data-pipeline and eval-harness engineering (e.g., Hugging Face Transformers, vLLM, lm-eval-harness).

Nice to Have

• Experience with MLflow or similar experiment-tracking frameworks.
• Familiarity with modern fine-tuning frameworks (Axolotl, TorchTune) and PyTorch-native training stacks (TorchTitan).
• Synthetic data generation techniques (e.g., Self-Instruct).
• Experience with preference optimization (DPO, RLHF, RLAIF, GRPO).
• Publications in NLP/ML/FAccT or related venues, or other evidence of research leadership.
• Experience evaluating multimodal models (embeddings, hateful-memes-style classification).

Benefits

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave