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Remote mid-level ML role but niche LLM and GPU expertise limits applicant pool.
LLM fine-tuning and GPU-serving skills are ML-specific but transferable across industries.
Explicit 5+ years plus mandatory LLM, PyTorch, and GPU production skills increase filtering.
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Fine-tune, evaluate, and optimize open-source and custom ML/deep-learning models for construction-related software use cases.
Build and maintain data pipelines, training loops, and evaluation harnesses, running experiments and iterating on model architecture and hyperparameters.
Work closely with applied and platform engineers to deploy, serve, and optimize models reliably at scale on GPUs across Azure and Google Cloud.
5+ years of experience in ML/AI with hands-on model training, fine-tuning, or evaluation.
Strong machine learning and deep learning fundamentals.
Proficiency in Python with frameworks like PyTorch or TensorFlow, and experience fine-tuning open-source models (LLMs/transformers).
Experience preparing models for production on GPUs including optimization techniques such as quantization and batching.
Experienced in deploying and optimizing ML models in a production environment using GPU infrastructure on cloud platforms (Azure/Google Cloud).
Comfortable iterating rapidly on models with strong experiment design and evaluation metric knowledge.
Has a Computer Science degree specialized in AI or equivalent practical experience, and can collaborate effectively across engineering, product, and design teams.