Member of Technical Staff

Location SF, NYC, London Employment Type Full time Location Type On-site Department Engineering Our Mission Reflection’s mission is to build open superintelligence and make it accessible to all . We’re developing open weight models for individuals, agents, enterprises, and even nation states. Our team of AI researchers and company builders come from DeepMind, OpenAI, Google Brain, Meta, Character.AI, Anthropic and beyond. About the Role Data is playing an increasingly crucial role at the frontier of AI innovation. Many of the most meaningful advances in recent years have come not from new architectures, but from better data. As a member of the Data Team, your mission is to ensure that the data used to train our models meets a high bar for quality, reliability, and downstream impact. You will directly shape how our models perform on critical capabilities. Working with world‑class researchers on our pre‑training teams, you’ll help turn fuzzy notions of “good data” into concrete, measurable standards that scale across large data campaigns. We’re looking for engineers who combine strong engineering fundamentals with a deep curiosity about data quality and its impact on model performance. Working closely with our pre‑training teams you will: Own upstream data quality for LLM pre‑training; as a specialist or generalist across languages and modalities Partner closely with research and pre‑training teams to translate requirements into measurable quality signals, and provide actionable feedback to external data vendors In addition to human‑in‑the‑loop processes, you will design, validate, and scale automated QA methods to reliably measure data quality across large campaigns Build reusable QA pipelines that reliably deliver high‑quality data to pre‑training teams for model training Monitor and report on data quality over time, driving continuous iteration on quality standards, processes, and acceptance criteria About You Strong engineering fundamentals with experience building data pipelines, QA systems, or evaluation workflows for pre‑training data Detail‑oriented with an analytical mindset, able to identify failure modes, inconsistencies, and subtle issues that affect data quality Solid understanding of how data quality impacts pre‑training, with the ability to translate quality concerns into concrete signals, decisions, and feedback Experience designing and validating automated quality checks, including rule‑based systems, statistical methods, or model‑assisted approaches such as LLM‑as‑a‑Judge Comfortable working autonomously, owning problems end‑to‑end, and collaborating effectively with researchers, engineers, and operations partners Skills and Qualifications Proficiency in Python and building ML / LLM workflows. Must be comfortable debugging and writing scalable code Experience working with large datasets and automated evaluation or quality‑checking systems Familiarity with how LLMs work and can describe how models are trained and evaluated Excellent communication skills with the ability to clearly articulate complex technical concepts across teams What We Offer: We believe that to build superintelligence that is truly open, you need to start at the foundation. Joining Reflection means building from the ground up as part of a small talent‑dense team. You will help define our future as a company, and help define the frontier of open foundational models. We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported. Top-tier compensation: Salary and equity structured to recognize and retain the best talent globally. Health & wellness: Comprehensive medical, dental, vision, life, and disability insurance. Life & family: Fully paid parental leave for all new parents, including adoptive and surrogate journeys. Financial support for family planning. Benefits & balance: paid time off when you need it, relocation support, and more perks that optimize your time. Opportunities to connect with teammates: lunch and dinner are provided daily. We have regular off‑sites and team celebrations. #J-18808-Ljbffr