Staff Research Engineer, Post-training & Evaluation

Reddit

12h • $230,000 - $322,000 • Remote

About The Position

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.

Requirements

• 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) — you know the strengths and limits of benchmarks like MMLU and GSM8K and when they don't apply, and you treat eval sets as versioned, frozen, regression-tracked code.
• 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). Working knowledge of PyTorch and distributed training (FSDP2, DeepSpeed ZeRO-3) sufficient to direct and debug post-training runs.

Nice To Haves

• 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).

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, 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).

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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior