LLM Training Engineer

Required skills

Strong general software engineering skills (writing robust, performant systems), Experience with training or serving large neural networks (LLMs or similar), Solid grasp of deep learning fundamentals and modern literature, Comfort working in high-performance environments (GPU, distributed systems, etc.), Relevant experience (one or more) Pretraining / large-scale distributed training (FSDP/ZeRO/Megatron-style systems), Post-training pipelines (SFT, RLHF/RLAIF, preference optimization, eval loops), Building RL environments, simulators, or agent frameworks, Inference optimization, model compression, quantization, kernel-level profiling, Building large ETL pipelines for internet-scale data ingestion and cleaning, Owning end-to-end production ML systems with monitoring and reliability, Research orientation, Ability to propose and evaluate research ideas quickly, Strong experimental hygiene: ablations, metrics, reproducibility, analysis, Bias toward building — you can turn ideas into working code and results

Preferred skills

MS or PhD in Computer Science, Machine Learning, AI, Mathematics, or related field

Technologies

GPU, distributed systems, large-scale distributed training (FSDP/ZeRO/Megatron-style systems), post-training pipelines (SFT, RLHF/RLAIF, preference optimization, eval loops), RL environments, simulators, or agent frameworks, inference optimization, model compression, quantization, kernel-level profiling, large ETL pipelines for internet-scale data ingestion and cleaning, end-to-end production ML systems with monitoring and reliability

Responsibilities

Pretraining & Scaling, Post-training & RL, Sandbox Environments & Evaluation, Deployment & Inference Optimization

Seniority

Domain

AI infrastructure, multimodal AI models, serving platform