LLM Training Engineer

Sciforium is an AI infrastructure company developing next-generation multimodal AI models and a proprietary, high-efficiency serving platform. As an LLM Training Engineer, you will work across the full foundation-model stack, focusing on pretraining, post-training, sandbox environments, and deployment optimization to deliver models for real-world applications at scale. Responsibilities: Train large byte-native foundation models across massive, heterogeneous corpora; Design stable training recipes and scaling laws for novel architectures; Improve throughput, memory efficiency, and utilization on large GPU clusters; Build and maintain distributed training infrastructure and fault-tolerant pipelines; Develop post-training pipelines (SFT, preference optimization, RLHF/RLAIF, RL); Curate and generate targeted datasets to improve specific model capabilities; Build reward models and evaluation frameworks to drive iterative improvement; Explore inference-time learning and compute techniques to enhance performance; Build scalable sandbox environments for agent evaluation and learning; Create realistic, high-signal automated evals for reasoning, tool use, and safety; Design offline + online environments that support RL-style training at scale; Instrument environments for observability, reproducibility, and iteration speed; Optimize inference throughput/latency for byte-native architectures; Build high-performance serving pipelines (KV caching, batching, quantization, etc.); Improve end-to-end model efficiency, cost, and reliability in production; Profile and optimize GPU kernels, runtime bottlenecks, and memory behavior Qualifications: 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.); 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; 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; MS or PhD in Computer Science, Machine Learning, AI, Mathematics, or related field Required Skills: Software engineering, Training large neural networks, Serving large neural networks, Deep learning fundamentals, GPU computing, Distributed systems, Pretraining large-scale distributed training, Post-training pipelines, Reinforcement learning, RL environments, Inference optimization, Model compression, Quantization, Kernel-level profiling, ETL pipelines, Production ML systems, Research orientation, Experimental hygiene