Machine Learning Infrastructure Engineer

reflection • United States

Visa Sponsorship

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AI Summary

Design and optimize core infrastructure for training frontier AI models, bridging research and production. Responsibilities include architecting scalable training systems, implementing advanced algorithms, and building tooling for complex experiments. Requires strong software engineering skills with ML understanding, and deep experience in Distributed Training & Inference or Data Infrastructure.

Key Highlights

Build and evolve core infrastructure for large-scale AI model training systems.

Work at the intersection of ML algorithms, distributed systems, and high-performance computing.

Ensure numerical stability, throughput, and reproducibility in massive-scale training.

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Key Responsibilities

Architect and optimize the core training infrastructure that powers our models, including RL training loops, distributed GPU systems, and large-scale data pipelines.

Work closely with researchers to transform new ideas into reliable, scalable training systems.

Design and optimize large-scale training loops and data pipelines.

Implement state-of-the-art techniques and ensure they are numerically stable and computationally efficient.

Build internal tooling for launching, monitoring, and reproducing complex experiments.

Diagnose deep bottlenecks across the training stack (GPU memory issues, communication overhead, dataloader stalls).

Translate research prototypes into reusable, production-grade infrastructure.

Technical Skills Required

Distributed Training Machine Learning Data Infrastructure

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Benefits & Perks

Top-tier compensation

Stock options

Comprehensive health & wellness

Paid parental leave

Unlimited paid time off (US)

30 days paid time off (UK)

Visa sponsorship support

Team building events

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Nice to Have

Experience with PyTorch, JAX, or Megatron-style training stacks.

Familiarity with Triton / custom kernels.

Experience with large-scale dataset curation pipelines, deduplication and filtering systems, tokenization and preprocessing, and distributed data processing frameworks.

Job Description

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.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.

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