Member of Technical Staff - Mid-Training Infra

Member of Technical Staff - Mid-Training Infra at reflectionai in San Francisco, CA, United States.

Role Summary

• 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.

Requirements

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.

Benefits

• 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.

About the Company

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.