Staff Research Scientist

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Snowflake's Data Engineering organization builds the platform that ingests, transforms, and stores data for modern lakehouse architectures — powering billions of queries, DML, and DDL operations with industry-leading price-performance. We lead the industry's shift to open data lakes through our work on Iceberg and Polaris, and we deliver capabilities like Snowpark, Dynamic Tables, cross-region replication, time travel, and zero-copy cloning at enterprise scale. We are investing in a new line of applied research — building toward verified data infrastructure and trustworthy data systems — that brings formal methods, automated reasoning, and modern AI techniques to bear on the hardest problems in our distributed systems and developer tooling. The goal is to improve correctness, reliability, and engineering velocity at a scale very few platforms operate at. We're hiring at both the Staff and Principal level; we'll calibrate the offer to the candidate's experience and scope of impact. WHAT YOU'LL DO * Lead research projects that apply formal methods, program analysis, automated reasoning, and AI-driven techniques (including code generation and modeling) to real problems in our cloud data platform. * Translate research ideas into prototypes, then into shipped capabilities that move concrete business metrics — quality, velocity, reliability, and operational performance at scale. * Partner closely with engineering leaders, product managers, and key customers to identify high-leverage opportunities and turn them into deliverables. * Influence the engineering and product roadmap; advise leaders on which research directions are pragmatic and which are not. * Train and uplevel engineering teams on new methods, and scale those methods across the organization. * Maintain expertise at the frontier of the field through publications, conference participation, open-source contributions, and patent filings. WHAT WE'RE LOOKING FOR * PhD (or equivalent research experience) in Computer Science or a closely related field. * Depth across the areas this role sits at the intersection of: * Formal methods — e.g., model checking, theorem proving, SAT/SMT, program verification, type systems, or program analysis. * Distributed systems — designing, reasoning about, or verifying large-scale concurrent and distributed systems. * Software engineering — strong fundamentals; able to go from a research idea to production-quality code in collaboration with engineering teams. * AI / ML — practical experience applying modern ML, including LLMs, to systems problems such as code generation, synthesis, or automated reasoning. * 8+ years applying theoretical computer science to large-scale software systems — ideally cloud data platforms, distributed systems, or developer infrastructure. * Demonstrated ability to drive company-level initiatives in partnership with engineering and product leadership. (Weighted more heavily for Principal-level candidates.) * Track record of technical contribution to the field — publications, open-source work, patents, or comparable evidence of impact. * Comfortable in a fast-paced, ambiguous environment where impact is measured by what ships. ABOUT WORKING HERE Every Snowflake employee is expected to follow the company's confidentiality and security standards for handling sensitive data, and to keep customer information secure and confidential as an essential part of their duties. Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake. How do you want to make your impact? For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com

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.