Member of Technical Staff - Pre-Training Infra

Reflection AI is hiring a Member of Technical Staff - Pre-Training Infra in San Francisco, United States. Level rates it ; you can apply on Reflection AI.

Level's read on this job

On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
The posting mentions visa sponsorship.
Level classes it as a lead role.
Reflection AI has 45 other open roles on Level.

AI in this role

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.

Equal Employment Opportunity Statement
Reflection AI is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), sexual orientation, gender identity or expression, national origin, ancestry, age, marital status, disability, genetic information, veteran or military status, or any other characteristic protected by applicable law. We encourage applicants from all backgrounds to apply.

We provide reasonable accommodations throughout the hiring process for applicants with disabilities, pregnancy-related limitations, or religious needs. If you need an accommodation, contact us at accommodations@reflection.ai.

We will consider for employment qualified applicants with arrest and conviction records in accordance with applicable law, including the San Francisco Fair Chance Ordinance and the California Fair Chance Act.

Candidate and Employee Privacy Notice: We collect and process personal data about applicants for the purposes described in our Candidate and Employee Privacy Notice

How we rate this

Member of Technical Staff - Pre-Training Infra at Reflection AI rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.