Member of Technical Staff - Pre-Training Infra

The role

You will build and scale distributed training systems that power frontier model pre-training. You will work with research teams to design and operate large-scale training runs for foundation models.

What you will do

• 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.

Required qualifications

• Experience building or operating distributed training systems for large machine learning models.
• Strong experience working with modern distributed training frameworks such as Megatron, DeepSpeed, 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.

Benefits

• Salary and equity
• Stock options
• Comprehensive medical, dental, vision, and life
• Annual wellness allowance
• Lunch and dinner provided in the office daily
• 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys
• Unlimited paid time off in the U.S.
• 30 days paid time off in the U.K.
• Visa sponsorship support
• Regular off-sites, happy hours, and team celebrations

Before you apply

• 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 contr

About the company

Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. They build open models that let anyone control their intelligence and help shape the future of AI.