Member of Technical Staff - Multilingual Data

Department: Research

Location: San Francisco, CA, London, New York, NY

Employment Type: FullTime

Our Mission

Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all.

About the Role

• Design and operate large-scale multilingual data pipelines — sourcing, cleaning, deduplication, language identification, and script normalization across high- and low resource languages.

• Define and enforce quality bars for multilingual corpora, including translation quality, cultural fidelity, toxicity, and contamination checks.

• Design and run scientific experiments to advance our understanding of scaling large language models to improve multilingual data efficiency.

• Lead small research projects independently while collaborating on larger initiatives.

• Build evaluation sets and diagnostics that expose where model behavior degrades by language, register, or domain, and close those gaps with targeted data.

• Work with pre-training, mid-training, and post-training teams to land measurable, step-function improvements in multilingual capability.

About You

• Strong software engineering fundamentals and comfort processing web-scale datasets in distributed environments.

• Experience building large-scale data pipelines for language models, machine translation, speech, or search — ideally covering more than one language.

• Fluency or working proficiency in at least one language other than English, and genuine curiosity about how languages differ.

• A rigorous, measurement-first mindset: you prove a data change helped rather than assuming it did.

• Navigate trade-offs between research objectives and practical engineering realities.

• Comfortable with ambiguity and high ownership in a small, fast-moving team. Passionate about advancing the frontier of intelligence.

NICE TO HAVE

• Graduate degree (MS or PhD) in Computer Science, Machine Learning, or related discipline.

• Published work in multilingual NLP, low-resource languages, or evaluation methodology.

• Experience with annotation vendor management or crowdsourced data operations at scale.

• Familiarity with tokenizer design and its effects on non-Latin scripts.

What We Offer:

We believe that to make intelligence open and accessible to all, you need to start at the foundation. Joining Reflection means building from the ground up as part of a talent-dense team. You will help define our future as a company, and help define the future 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 our talent globally.

• Stock options: Everyone who joins and contributes to Reflection's success gets to share in the upside through stock options.

• Health & wellness: Comprehensive medical, dental, vision, and life, with an annual wellness allowance.

• Meals: Lunch and dinner are provided in the office daily.

• Life & family: 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys.

• Vacation days: Unlimited paid time off in the U.S. and 30 days in the U.K.

• Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable.

• Team building: We have regular off-sites, happy hours, and team celebrations.

Export Control Notice: This position may require access to technology or source code subject to the U.S. Export Administration Regulations. Any offer of employment for this role may be conditioned on the Company's ability to provide the candidate with access to such technology or source code in compliance with applicable U.S. export control laws, which may require the Company to seek government authorization.