Machine Learning Engineer

Job Description

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We’re looking for a Senior AI/ML Research Engineer to work on large-scale AI inference, synthetic data generation, and distributed training systems.

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This is a hands-on research and engineering role for someone who has built infrastructure supporting large-scale AI models and wants to work on difficult problems across inference performance, distributed systems, reinforcement learning, and synthetic data.

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No third parties

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What You’ll Do

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Design and build large-scale synthetic data generation pipelines and orchestration systems.

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Optimize AI inference workloads for performance, cost, memory, and compute utilization.

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Build and improve distributed inference infrastructure for large-scale AI models.

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Contribute to open-source libraries and frameworks for synthetic data generation and distributed reinforcement learning.

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Research and implement new approaches to model inference, training, and post-training.

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Collaborate with researchers and engineers working on large-scale AI systems.

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Contribute research suitable for publication at conferences such as ICML and NeurIPS.

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Translate highly technical work into clear technical content for developers and users.

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Stay current with advances in AI infrastructure, distributed inference, synthetic data, and model optimization.

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What We’re Looking For

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• Strong AI/ML engineering background with experience building production systems for large-scale model inference or training.
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• Experience designing and implementing end-to-end AI/ML pipelines.
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• Deep understanding of distributed inference and optimization of large-model workloads.
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• Hands-on experience with inference frameworks such as vLLM or SGLang .
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• Strong understanding of GPU compute, memory optimization, throughput, latency, and resource utilization.
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Experience with one or more of the following is especially valuable:

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• LLM inference infrastructure
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• vLLM
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• SGLang
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• Distributed inference
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• Distributed training
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• Reinforcement learning
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• Synthetic data generation
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• GPU optimization
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• Model post-training
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• Large-scale ML systems
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