At a Glance
- Tasks: Build and scale distributed training systems for cutting-edge AI models.
- Company: Join a mission-driven team from top AI companies like DeepMind and OpenAI.
- Benefits: Top-tier salary, comprehensive health benefits, and generous parental leave.
- Other info: Collaborative environment with daily meals and regular team celebrations.
- Why this job: Make a real impact in AI by developing open superintelligence technologies.
- Qualifications: Experience with distributed training systems and modern ML frameworks.
The predicted salary is between 75600 - 92400 £ per year.
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 Deep Mind, Open AI, Google Brain, Meta, Character.
AI, Anthropic and beyond.
About The Role
- Build and scale distributed training systems that power frontier model pre-training.
- Work closely with research teams to design and operate large-scale training runs for foundation models.
- Develop infrastructure that enables efficient training across thousands of GPUs using modern distributed training frameworks.
- Optimize training throughput, stability, and efficiency for large model training workloads.
- Collaborate directly with pre-training researchers to translate experimental ideas into scalable, production-ready training systems.
- Improve performance of distributed training workloads through optimization of communication, memory usage, and GPU utilization.
- Build and maintain training pipelines that support large-scale datasets, checkpointing, and experiment iteration.
- Debug and resolve performance bottlenecks across distributed training stacks including model parallelism, GPU communication, and training runtime systems.
- Contribute to the development of systems that enable rapid experimentation and iteration on new training techniques.
- Ideal Experience
- Experience building or operating distributed training systems for large machine learning models.
- Strong experience working with modern distributed training frameworks such as Megatron, Deep Speed, or similar large-scale training systems.
- Familiarity with large-scale model parallelism strategies (data, tensor, pipeline, or expert parallelism).
- Experience optimizing training throughput and GPU utilization in large distributed environments.
- Familiarity with GPU communication libraries such as NCCL and performance tuning for distributed workloads.
- Experience working closely with ML researchers to productionize experimental training workflows.
- Strong debugging skills across GPU compute, distributed training systems, and large-scale ML pipelines.
- Experience working with large datasets and training pipelines used for foundation model pre-training.
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.
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Member of Technical Staff - Pre-Training Infra employer: Reflection
At Reflection, we are committed to fostering a dynamic and inclusive work environment where innovation thrives. As a Member of Technical Staff - Data Ingestion Engineer, you will be part of a small, talent-dense team dedicated to building open superintelligence, with access to top-tier compensation, comprehensive health benefits, and generous parental leave. Our culture prioritises collaboration and personal growth, ensuring that you can make a meaningful impact while enjoying a supportive work-life balance in a cutting-edge field.
StudySmarter Expert Advice🤫
We think this is how you could land Member of Technical Staff - Pre-Training Infra
✨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 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.
✨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.
✨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 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 - Pre-Training Infra
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.
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 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
✨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 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.
