Senior Applied Machine Learning Engineer

Senior Applied Machine Learning Engineer About Sub Base

Sub Base is revolutionizing construction procurement by streamlining material management for subcontractors and self‑performing general contractors. Our platform replaces fragmented workflows with a unified, user‑friendly solution that enhances efficiency without disrupting existing processes. By connecting field teams, office staff, and vendors, we empower construction professionals to manage procurement seamlessly.

We are seeking an experienced Senior Applied Machine Learning Engineer to drive the development and integration of AI‑driven solutions within our platform. This role involves leveraging existing AI models and creating custom algorithms to optimize procurement processes, enhance decision‑making, and deliver actionable insights for our users.

• Design, develop, and deploy machine learning models tailored to solving challenges faced by many players within the construction industry.
• Work closely with cross‑functional teams, including product managers, software engineers, and domain experts, to align AI solutions with business objectives.
• Implement monitoring systems to evaluate model performance, ensuring accuracy, reliability, and impact towards business objectives.
• Utilize APIs from providers like OpenAI and Google Gemini to incorporate advanced AI capabilities into our platform.
• Build and maintain robust data pipelines to support model training and real‑time analytics.
• Stay abreast of the latest developments in AI and machine learning to continuously enhance our platform’s capabilities.
• Own the full lifecycle of LLM prompt development — from dataset curation and test harness setup (e.g., Promptfoo) to model comparison and performance tuning.

• 6+ years in machine learning engineering, with a proven track record of building and deploying models in production environments.
• At least Master’s degree in Computer Science, Data Science, or a related field.

• Proficiency in programming languages such as Python and Ruby.
• Strong understanding of data structures, data modeling, and software architecture.
• Experience building production level data pipelines that utilize internal and external data to support models in production.
• Experience leveraging LLM, computer vision, and other external models such as GPT, Gemini in building production level applications.
• Experience with machine learning frameworks like Tensor Flow, PyTorch, or scikit‑learn.
• Experience with deploying ML models in production environments, particularly within a Ruby on Rails stack.
• Familiarity with cloud platforms (AWS, GCP, Azure) for scalable AI/ML deployments.
• MVP centric mentality, focused on delivering impact with speed.
• Excellent problem‑solving abilities and analytical skills.
• Strong communication skills, with the ability to convey complex technical concepts to non‑technical stakeholders.
• Customer‑focused and highly collaborative — proactively tackle small and large responsibilities with a positive attitude and an open mindset to help lead and learn from partner teams.