Senior Research Engineer, Post-training & Evaluation

Responsibilities

• Define the "Reddit Benchmark" evaluation standard: Own the methodology 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.
• Set post-training recipes and strategy: Design SFT recipes (data mixtures, curriculum, ablation strategy) that convert base models into helpful, well-aligned endpoints.
• Evaluate base and CPT checkpoints: Design checkpoint-selection methodology across CPT experiments and LR studies.
• 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.
• Diagnose post-training instability: Dive into loss curves and eval logs to identify alignment tax and capability degradation.
• Lead research direction: Set technical direction for evaluation and post-training across the team, mentor engineers and scientists.

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).
• 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).

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).