Reflection
Member of Technical Staff - Pre-Training Infra
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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.
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.
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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, 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.
What We Offer
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.
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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.
Candidate and Employee Privacy Notice
We collect and process personal data about applicants for the purposes described in our Candidate and Employee Privacy Notice.
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