Machine Learning Engineer

What You’ll Do: Design and build large-scale synthetic data generation pipelines and orchestration systems. Optimize AI inference workloads for performance, cost, memory, and compute utilization. Build and improve distributed inference infrastructure for large-scale AI models. Contribute to open-source libraries and frameworks for synthetic data generation and distributed reinforcement learning. Research and implement new approaches to model inference, training, and post-training. Collaborate with researchers and engineers working on large-scale AI systems. Contribute research suitable for publication at conferences such as ICML and NeurIPS.Translate highly technical work into clear technical content for developers and users. Stay current with advances in AI infrastructure, distributed inference, synthetic data, and model optimization.

What We’re Looking For: Strong AI/ML engineering background with experience building production systems for large-scale model inference or training. Experience designing and implementing end-to-end AI/ML pipelines. Deep understanding of distributed inference and optimization of large-model workloads. Hands-on experience with inference frameworks such as vLLM or SGLang. Strong understanding of GPU compute, memory optimization, throughput, latency, and resource utilization. Experience with one or more of the following is especially valuable: LLM inference infrastructurevLLMSGLang Distributed inference Distributed training Reinforcement learning Synthetic data generationGPU optimization Model post-training Large-scale ML systems