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

B Capital; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Top-tier compensation

Comprehensive medical, dental, vision insurance

Fully paid parental leave

Paid time off

Daily lunch and dinner provided

Job summary

B Capital is seeking a data engineer to ensure high data quality for training AI models. You will own the upstream data quality for LLM post-training and design automated QA methods in a collaborative environment. Ideal candidates will have strong engineering skills, a detail-oriented approach, experience with Python, and proficiency in building ML workflows. The role offers top-tier compensation, comprehensive health benefits, and a supportive work environment focused on career impact.

Qualifications

• Strong engineering fundamentals with experience building data pipelines.
• Detail-oriented with an analytical mindset.
• Solid understanding of data quality's impact on training.

Responsibilities

• Own upstream data quality for LLM post-training.
• Partner with research teams to translate requirements into measurable quality signals.
• Design and scale automated QA methods.

Skills

Proficiency in Python

Experience with large datasets

Automated evaluation systems

Excellent communication skills

Job description

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 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 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.