Member of Technical Staff - Data Quality Engineer (Post-training)

reflectionai; San Francisco, New York (CA, NY); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Top-tier compensation

Stock options

Health & wellness

Meals provided

Parental leave

Unlimited vacation (US)

Visa sponsorship

Team events

Job summary

Reflectionai in San Francisco is seeking a Data Quality Engineer to join the Data Team and ensure high-quality data for post‑training and evaluation. You will shape how data quality affects model capabilities in agentic tool use, long‑horizon reasoning, and safety alignment.

You will work with researchers and engineers to translate quality requirements into measurable signals, design scalable QA methods, and build pipelines that reliably deliver data for training and assessment.

Qualifications

• Strong engineering fundamentals in data pipelines and QA systems.
• Analytical mindset with ability to spot data quality issues.
• Experience with automated quality checks and LLM‑as‑a‑Judge.

Responsibilities

• Own upstream data quality for post‑training and evaluation.
• Translate requirements into measurable quality signals for data vendors.
• Design, validate, and scale automated QA methods for large campaigns.
• Build reusable QA pipelines delivering high‑quality data for training and evaluation.
• Monitor data quality over time and iterate on standards and criteria.

Skills

Python proficiency

ML/LLM workflows

Data pipelines

Automated evaluation

Communication skills

Job description

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

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 and evaluate our models meets a high bar for quality, reliability, and downstream impact. You will directly shape how our models perform on critical capabilities — agentic tool use, long‑horizon reasoning and robust safety alignment.

Working with world‑class researchers on our post‑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 behavior

Working closely with our post‑training teams you will:

• Own upstream data quality for LLM post‑training and evaluation by analyzing expert‑developed datasets and operationalizing quality standards for reasoning, alignment, and agentic use cases

• Partner closely with research and post‑training teams to translate requirements into measurable quality signals, and provide actionable feedback to external data vendors

• Design, validate, and scale automated QA methods, including LLM‑as‑a‑Judge frameworks, to reliably measure data quality across large campaigns

• Build reusable QA pipelines that reliably deliver high‑quality data to post‑training teams for model training and evaluation

• 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 post‑training data and agentic environments

• 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 training (SFT and RL) and evaluation, 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 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.