Machine Learning Engineer

At Sure Steel, Inc., we are more than just a construction company. We are builders of success, integrity, and innovation. Our leadership in steel fabrication and construction stems from our commitment to excellence, safety, and customer satisfaction. Our AI Strategy Department plays a crucial role in delivering next-generation solutions that enhance safety, efficiency, and performance across our projects. 

We are hiring a Machine Learning Engineer to lead Sure Steel's work in building, training, and deploying our own AI models. This is a new role for the company. The Machine Learning Engineer will fine-tune and run open-weight models on Sure Steel's own documents, data, and workflows, creating AI trained not just on steel erection, but on Sure Steel specifically. 

The work focuses on high-volume, company-specific tasks such as forecasting, scheduling, document reading and classification, data extraction, and help desk support. These models will lower AI operating costs, keep sensitive data on infrastructure we control, and reduce dependence on any single AI provider. This role works closely with the Wayne development team and provides technical leadership in machine learning, but does not include formal supervisory responsibilities.

• Identify, scope, and prioritize machine learning use cases with the Senior AI Strategist, starting small and proving value before scaling. 

• Select, evaluate, and fine-tune open-weight language and vision models on Sure Steel documents, drawings, and operational data. 

• Build and maintain training datasets, including data collection, cleaning, labeling, and versioning, in partnership with the data owners in each department. 

• Develop models for forecasting, scheduling, document classification, data extraction, and drawing interpretation. 

• Deploy models into production on cloud or company-controlled infrastructure, and integrate them with Wayne and other internal systems through APIs. 

• Design routing so high-volume, repetitive work runs on cost-efficient in-house models. 

• Track and report model accuracy, cost per task, and savings against frontier model usage, so each use case shows it pays for itself. 

• Monitor model performance in production, retrain as data and processes change, and maintain clear documentation for every model. 

• Protect sensitive data, including HR, payroll, and time tracking information, through secure handling, access controls, and alignment with company cybersecurity standards. 

• Manage cloud compute for training and inference, keeping spend efficient and predictable. 

• Stay current on open-weight models, tooling, and research, and recommend new approaches as the field moves. 

• Share knowledge with the Wayne development team and help build the company's internal machine learning capability. 

Qualifications

• Education: Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, Statistics, Engineering, or a related field, or equivalent practical experience. 

• Hands-on experience training, fine-tuning, and evaluating machine learning models, including large language models. 

• Strong proficiency in Python and common ML frameworks (PyTorch, Hugging Face Transformers, or similar). 

• Experience with fine-tuning techniques such as LoRA, QLoRA, or other parameter-efficient methods. 

• Experience preparing datasets: collection, cleaning, labeling, and evaluation set design. 

• Experience deploying models to production, including serving, APIs, and monitoring. 

• Solid foundation in statistics and model evaluation. 

• Strong communication skills, with the ability to explain technical tradeoffs to business leaders. 

Preferred Skills 

• Experience running open-weight models with tools such as vLLM, Ollama, or llama.cpp, including quantization for cost-efficient inference. 

• Computer vision or document AI experience, especially reading drawings, scanned documents, or forms. 

• Experience with retrieval-augmented generation (RAG), embeddings, and vector databases. 

• Familiarity with MLOps practices: experiment tracking, model versioning, and CI/CD for models. 

• Experience with time-series forecasting. 

• Experience using AI coding tools (Claude Code or similar) as part of a daily workflow. 

• Experience with construction tech or ERP systems (e.g., Tekla, Spectrum, Procore). 

• Industry experience in construction, manufacturing, or industrial operations.