Site Reliability Engineer

Accelerate Enterprise Intelligence as an AI/ML Engineer (P1)

Are you passionate about the foundational elements of machine learning and eager to contribute to innovative AI solutions within a collaborative, growth-oriented team environment? Trimble is seeking an enthusiastic AI/ML Engineer (P1) to join our Construction Management Solutions (CMS) team. This role offers an exceptional runway to accelerate your career in AI/ML engineering, working directly alongside seasoned data scientists and principal engineers to transform complex real-world data into actionable, production-grade enterprise tools.

About Our Division: Construction Management Solutions (CMS):

As an industry leader in construction software and ERP solutions, Trimble CMS empowers contractors worldwide. Our solutions seamlessly connect the office, team, and jobsite by integrating essential business functions like accounting, project management, and field operations. Operating on the Trimble Construction One (TC1) connected ecosystem, our collaborative cloud platforms enhance transparency, efficiency, and profitability for organizations of all sizes across the globe.

What Makes This Role Great:

This role provides a structured, high-support environment designed to build deep competencies in applied machine learning, data engineering, and modern MLOps under direct mentorship. Working within our CMS engineering organization, you will gain hands-on exposure to rich industrial datasets, participate across the full AI project lifecycle, and contribute to cutting-edge model integration. Whether you are stepping in as an Associate Engineer (P1), you will enjoy real technical ownership, contributing to software tools that drive physical-world operations globally.

Key Exciting Responsibilities:

• Algorithm & Model Development: Assist in developing, implementing, and fine-tuning machine learning algorithms and foundational models, with direct exposure to advanced deep learning paradigms.
• Data Engineering & Preprocessing: Own data collection, sanitization, exploratory analysis, and feature engineering tasks to ensure data quality, consistency, and industry-specific relevance.
• Testing, Verification & Code Health: Execute routine testing, debugging, and comprehensive documentation of ML codebases and pipelines to ensure reproducible, high-performance systems.
• ML Integration & Operationalization: Support the integration of trained models into enterprise-grade applications, adhering to standard architectural patterns while learning essential MLOps practices such as containerization, artifact tracking, and performance monitoring.
• Cross-Functional Collaboration: Partner with senior engineers and domain specialists to translate ambiguous business requirements into well-defined machine learning tasks and actionable delivery milestones.

Essential Skills & Experience:

• Education: Bachelor's degree in Computer Science, Mathematics, Engineering, Statistics, or a closely related quantitative field (Trimble's Professional ladder typically requires four or more years of formal education).
• Core ML Literacy: Strong conceptual grounding in machine learning theory, evaluation metrics, statistical techniques, and foundational deep learning concepts.
• Programming Proficiency: Strong coding skills in Python, with practical experience manipulating datasets and building algorithms using standard data science libraries including NumPy, pandas, and scikit-learn.
• Computer Science Foundations: Solid grasp of fundamental data structures, basic data modeling principles, and software engineering practices necessary for clean, modular code development.
• Analytical Problem Solving: Proven ability to follow structured engineering methodologies to troubleshoot issues of defined scope and arrive at clear, practical solutions.

Bonus Points For:

• Deep Learning Frameworks: Hands-on familiarity with modern deep learning toolkits such as PyTorch or TensorFlow.
• Cloud Computing Platforms: Basic foundational understanding of cloud environments (AWS, Microsoft Azure, or GCP) and their AI/ML service offerings.
• Development Workflow Tools: Experience utilizing version control systems (Git) for collaborative branch-based development, alongside basic Linux CLI and Bash scripting comfort.
• Applied Portfolio: Early evidence of production-oriented ML work demonstrated through capstone projects, research publications, or active GitHub repositories.
• Generative AI Awareness: Academic exposure to, or experimentation with, Generative AI models, transformer architectures, and Large Language Models (LLMs).