Machine Learning Infrastructure Engineer

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.

The Roles Mission

Bridge the gap between research and production by turning cutting-edge algorithms into scalable training systems. You will design and optimize the core infrastructure behind frontier AI models — from reinforcement learning training loops and distributed GPU training to massive-scale data pipelines.

Our systems train models across thousands of GPUs and process petabyte-scale datasets. We care deeply about numerical stability, throughput, and reproducibility.

What This Team Does

This team owns and evolves the core infrastructure behind our training systems.

We Focus On

• Reinforcement learning training infrastructure
• Distributed training and inference systems
• Experiment infrastructure and reproducibility
• Large-scale data pipelines
  
  

The goal is to build the engineering foundation that allows researchers to iterate quickly while training models at massive scale.

About The Role

You will architect and optimize the core training infrastructure that powers our models. This includes RL training loops, distributed GPU systems, and large-scale data pipelines.

You will work closely with researchers to transform new ideas into reliable, scalable training systems.

Responsibilities Include

• Designing and optimizing large-scale training loops and data pipelines.

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• Implementing state-of-the-art techniques and ensuring they are numerically stable and computationally efficient.
• Building internal tooling for launching, monitoring, and reproducing complex experiments.
• Diagnosing deep bottlenecks across the training stack (GPU memory issues, communication overhead, dataloader stalls).
• Translating research prototypes into reusable, production-grade infrastructure.
  
  

What You'll Work With

Distributed Training

• GPU parallelism (data, tensor, pipeline, expert)
• Large-scale distributed training infrastructure
• Communication optimization (NCCL, RDMA, GPU interconnects)
• FSDP / ZeRO and model sharding
  
  

Orchestration & Runtime Systems

• Ray, Kubernetes, Slurm
• Distributed runtimes and async systems
• Containerization and sandboxing
  
  

Frameworks

• PyTorch
• JAX
• Megatron-style training stacks
• Triton / custom kernels
  
  

Data Infrastructure

• Large-scale dataset curation pipelines
• Deduplication and filtering systems
• Tokenization and preprocessing
• Distributed data processing frameworks
  
  

About You

• You are a strong software engineer who speaks the language of machine learning.
• You may not have a PhD, but you know how to implement a research paper.
• You have deep experience in at least one of the following: Distributed Training & Inference or Data Infrastructure
• You enjoy working at the boundary between:
• Machine learning algorithms

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• Distributed systems
• High-performance computing
• You care deeply about performance, numerical stability, and reproducibility.
• You thrive in high-agency environments and enjoy solving hard technical problems.
  

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.