Reddit · Remote - United States
Staff Research Engineer, Post-training & Evaluation
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 intern...
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, 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) — 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 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
#LI-SP1
Pay Transparency:
This job posting may span more than one career level.
In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the
position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to
U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for
vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/.
To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We
set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage
growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant
licenses/credentials, and may vary from the amounts listed below.
The base salary range for this position is:
$230,000—$322,000 USD
In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You
will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.
During the interview, we will collect the following categories of personal information: Identifiers, Professional and
Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you
choose to share with us. We will use this information to evaluate your application for employment or an independent contractor
role, as applicable. We will not sell your personal information or disclose it to any third party for their marketing purposes.
We will delete any recording of your interview promptly after making a hiring decision. For more information about how we will
handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential
Employees and Contractors.
Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse
communities we serve. Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and
disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview
process, please let your recruiter know.
Roll
Staff Research Engineer, Post-training & Evaluation
Kategori
Data/IT
Plats
Publicerad
8 juli
Sista ansökningsdag
17 augusti
Källa