At a Glance
- Tasks: Design and operate large-scale GPU infrastructure for cutting-edge AI workloads.
- Company: Join a leading tech company at the forefront of AI innovation.
- Benefits: Top-tier salary, comprehensive health benefits, and generous parental leave.
- Other info: Enjoy daily meals, team celebrations, and excellent career growth opportunities.
- Why this job: Make a real impact in AI by optimising high-performance systems.
- Qualifications: Experience with large-scale GPU systems and modern inference frameworks.
The predicted salary is between 80000 - 100000 £ per year.
About The Role
Design, build, and operate large-scale GPU infrastructure for high-throughput model inference and mid-training workloads. Develop systems that power synthetic data generation and reinforcement learning pipelines at scale. Build high-performance inference platforms capable of serving and evaluating models across thousands of GPUs. Optimize throughput, latency, and GPU utilization for large language model inference and rollout workloads. Build infrastructure that supports reinforcement learning pipelines, including large-scale rollout generation, evaluation, and policy improvement loops. Work closely with research teams to support distributed RL workloads and large-scale model evaluation infrastructure. Improve performance of model execution through kernel-level optimization, model parallelism strategies, and GPU runtime improvements. Develop distributed systems that enable large-scale synthetic data generation and RL-driven training workflows. Diagnose and resolve performance bottlenecks across inference runtimes, GPU kernels, networking, and distributed compute systems.
Ideal Experience
- Experience deploying and operating large-scale GPU systems for inference or model serving.
- Several years of hands-on experience building and running production infrastructure.
- Strong understanding of GPU performance characteristics and optimization techniques.
- Experience working with modern inference frameworks such as SGLang, Megatron, or similar high-performance LLM runtimes.
- Familiarity with distributed reinforcement learning infrastructure or rollout generation systems.
- Experience optimizing throughput for large-scale model execution workloads.
- Experience working with GPU kernels or low-level performance optimization.
- Familiarity with infrastructure used for synthetic data pipelines or RL training workflows.
- Experience debugging performance issues across GPU, networking, and distributed execution layers.
What We Offer
- 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.
Member of Technical Staff - Mid-Training Infra employer: Reflection
Join a forward-thinking company that excels in building cutting-edge GPU infrastructure, offering an environment where innovation thrives. With top-tier compensation, comprehensive health benefits, and a strong focus on work-life balance, employees are empowered to grow both personally and professionally. The collaborative culture fosters connections through daily meals and regular team celebrations, making it an ideal place for those seeking meaningful and rewarding employment in the tech industry.
StudySmarter Expert Advice🤫
We think this is how you could land Member of Technical Staff - Mid-Training Infra
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects related to GPU infrastructure and model inference. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your knowledge of GPU performance characteristics and optimization techniques. Practice coding challenges and system design questions that relate to large-scale systems.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team!
We think you need these skills to ace Member of Technical Staff - Mid-Training Infra
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the job description. Highlight your experience with GPU systems and any relevant projects you've worked on. We want to see how you can contribute to our team!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about the role and how your background aligns with our needs. Be genuine and let your personality come through – we love seeing the real you!
Showcase Relevant Projects:If you've worked on any projects related to large-scale GPU infrastructure or reinforcement learning, make sure to mention them. We’re interested in practical experience, so don’t hold back on the details!
Apply Through Our Website:We encourage you to apply directly through our website for the best chance of getting noticed. It’s super easy, and you’ll be one step closer to joining our awesome team at StudySmarter!
How to prepare for a job interview at Reflection
✨Know Your GPUs
Make sure you brush up on your knowledge of GPU performance characteristics and optimisation techniques. Be ready to discuss specific examples from your experience where you've deployed or operated large-scale GPU systems, as this will show your practical understanding of the role.
✨Familiarise with Inference Frameworks
Get to grips with modern inference frameworks like SGLang or Megatron. If you’ve worked with high-performance LLM runtimes, prepare to share insights on how you optimised throughput for large-scale model execution workloads. This will demonstrate your hands-on experience and technical expertise.
✨Showcase Problem-Solving Skills
Be ready to discuss how you've diagnosed and resolved performance bottlenecks in previous roles. Think about specific challenges you faced with GPU kernels, networking, or distributed compute systems, and how you overcame them. This will highlight your analytical skills and ability to think critically under pressure.
✨Collaborate and Communicate
Since the role involves working closely with research teams, be prepared to talk about your experience in collaborative environments. Share examples of how you’ve supported distributed RL workloads or contributed to large-scale model evaluation infrastructure. Strong communication skills are key, so make sure to convey your ideas clearly.
