Job Details

100% Remote

Job Title: AI/ML Engineer - Large Language Model Pretraining (100B+ Parameters)
Log-line: Gen AI Engineer creating and developing LLMs from "Test to Production".
Location - West Coast 100% Remote

Role Overview
As a Gen AI Engineer, you will lead the pre-training of massive LLMs (100B+ parameters) , requiring deep expertise in distributed training, large-scale optimization, and model architecture. This is a rare opportunity to work with petabyte-scale datasets and cutting-edge compute clusters in a high-impact environment.

Key Responsibilities

• Architect and implement large-scale training pipelines for LLMs with 100B+ parameters.
• Optimize distributed training performance across thousands of GPUs/TPUs.
• Collaborate with research scientists to translate experimental results into production-grade training runs.
• Manage and preprocess petabyte-scale datasets for pretraining.
• Implement state-of-the-art techniques in scaling laws, model parallelism, and memory optimization.
• Conduct rigorous benchmarking, profiling, and performance tuning.
• Contribute to Client research in LLM architecture, training stability, and efficiency.

Required Qualifications

• Advanced degree (PhD or Master's) in Computer Science, Machine Learning, or related field from top 20 global university in CS.
• 3+ years of hands-on experience with large-scale deep learning model training.
• Proven experience in pretraining models exceeding 10B parameters , preferably 100B+.
• Deep expertise in distributed training frameworks ( DeepSpeed, Megatron-LM, PyTorch FSDP, TensorFlow Mesh, JAX/TPU ).
• Proficiency with parallelism strategies (data, tensor, pipeline) and mixed precision training .
• Experience with large-scale cloud or HPC environments ( AWS, Azure, GCP, Slurm, Kubernetes, Ray ).
• Strong skills in Python , CUDA , and performance optimization.
• Strong publication record in top-tier ML/AI venues (NeurIPS, ICML, ICLR, ACL, etc.) preferred.

Preferred Skills

• Experience with LLM fine-tuning (RLHF, LoRA, PEFT).
• Familiarity with tokenizer development and multilingual pretraining.
• Knowledge of scaling laws and model evaluation frameworks for massive LLMs.
• Hands-on work with petabyte-scale distributed storage systems .

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