Member of Technical Staff - Pre-Training Infra

Sierra Ventures; San Francisco (CA); Presencial; USD 180.000 - 300.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Top-tier compensation

Stock options

Health & wellness

Meals provided in office

Parental leave

Descripción de la vacante

Reflection is a research lab focused on open, accessible AI, seeking an experienced engineer to build and scale distributed training systems for frontier model pre-training. You will work with researchers to operate large-scale runs across thousands of GPUs, optimizing throughput and efficiency while translating experiments into production-ready training pipelines.

The role emphasizes collaboration with ML researchers, debugging across GPU compute stacks, and improving distributed training

Formación

• Experience building distributed training systems for large ML models.
• Proficiency with Megatron, DeepSpeed or equivalent frameworks.
• Expertise in GPU utilization and NCCL for high-performance training.
• Strong debugging skills across distributed training stacks.
• Ability to collaborate with ML researchers to productionize training workflows.

Responsabilidades

• Build and scale distributed training systems powering frontier model pre-training.
• Operate large-scale training runs across thousands of GPUs.
• Optimize training throughput, memory usage, and GPU utilization.
• Develop scalable training pipelines with checkpointing and experiment management.
• Debug performance bottlenecks across distributed stacks such as model parallelism and GPU communication.

Conocimientos

Distributed training

Megatron/DeepSpeed

GPU optimization

NCCL

Debugging GPUs

Research collaboration

Herramientas

Megatron-LM

DeepSpeed

Descripción del empleo

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.
  

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.

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