Member of Technical Staff, Data Flywheel

Member of Technical Staff, Data Flywheel

Full-Time 63000 - 77000 £ / year (est.) On-site
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At a Glance

  • Tasks: Join our Data Flywheel team to enhance AI model performance through innovative data strategies.
  • Company: Reflection, a cutting-edge research lab focused on open intelligence.
  • Benefits: Top-tier salary, stock options, comprehensive health benefits, and unlimited vacation days.
  • Other info: Enjoy a supportive culture with team-building events and opportunities for career growth.
  • Why this job: Make a real impact in AI while collaborating with talented professionals in a dynamic environment.
  • Qualifications: Degree in Computer Science or related field, strong software engineering skills, and experience in ML systems.

The predicted salary is between 63000 - 77000 £ per year.

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.

Role Overview

The Data Flywheel team closes the gap between benchmark performance and useful performance in the real world. We identify and build the signals, data, and feedback loops that turn model usage into rigorous evaluations, targeted training data, and measurable improvements in future generations of models. This is a hands-on technical role at the intersection of research and deployment. You'll take ambiguous model behaviours from first observation through measurement, intervention, and validated improvement, working across evaluation, human and synthetic data, infrastructure, post-training, and live deployments. You'll collaborate closely with researchers and engineers across the company, as well as customers, partners, vendors, and the open-source community.

What You’ll Do

  • Identify high-value data sources and partnership opportunities, deeply understand the underlying use cases, and translate them into representative evaluations.
  • Bring new data sources online, from initial partner conversations and data scoping through quality validation and integration into production evaluation and training pipelines, where appropriate.
  • Design and build evaluations, graders, and feedback loops that make priority real-world model behaviours measurable.
  • Analyse model performance and failure modes, then translate those insights into targeted datasets, reward signals, and training interventions.
  • Develop human and synthetic data strategies for capabilities where existing data is insufficient, including designing and running evaluation and data-collection programs with vendors.
  • Build the infrastructure and pipelines needed to ingest, inspect, version, and evaluate data reliably at scale.
  • Collaborate closely with pre-training, post-training, applied, and partnership teams to turn new signals into measurable model improvements.

What We’re Looking For

  • Degree (BS, MS, or PhD) in Computer Science, Machine Learning, or related discipline, or equivalent practical experience.
  • Deep technical understanding of LLM training and evaluation, with hands-on experience in areas such as evaluation design, data curation, reinforcement learning, or reward design.
  • Strong software engineering skills and experience building automated data/evaluation pipelines or large-scale ML systems.
  • A track record of owning high-impact projects end to end, navigating ambiguity, and adapting quickly as priorities change.
  • A highly collaborative, action-oriented approach and excitement about defining how a new frontier lab measures and accelerates model progress.
  • High agency and thrive in a fast-paced startup environment; bias for impact over process.
  • Enjoy collaborating across research, engineering, operations, and product disciplines.

What We Offer:

  • 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.

Member of Technical Staff, Data Flywheel employer: Reflection AI

At Reflection, we are committed to making intelligence open and accessible for everyone, fostering a collaborative and innovative work culture that empowers our employees to make a meaningful impact. As a member of the Data Flywheel team, you will have the opportunity to work at the cutting edge of AI research and deployment, with access to top-tier compensation, unlimited paid time off in the U.K., and comprehensive health benefits. Join us in shaping the future of open foundational models while enjoying a supportive environment that prioritises your growth and well-being.

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Contact Details:

Reflection AI Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Member of Technical Staff, Data Flywheel

✨Join Local Tech Meetups

Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Reflection AI or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!

✨Contribute to Open Source Projects

Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Reflection AI.

✨Tap into Online Developer Communities

Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Reflection AI.

✨Explore Job Boards Specifically for Tech Roles

Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Reflection AI that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!

We think you need these skills to ace Member of Technical Staff, Data Flywheel

Data Analysis
Machine Learning
Evaluation Design
Data Curation
Reinforcement Learning
Reward Design
Software Engineering

Some tips for your application 🫡

Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.

Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Reflection AI.

Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Reflection AI and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!

Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!

How to prepare for a job interview at Reflection AI

✨Brush Up on Your Coding Skills

For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.

✨Know Your Tools and Frameworks

Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Reflection AI uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.

✨Showcase Your Projects

Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.

✨Prepare for Behavioural Questions

While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.