Machine Learning Engineer

Overview

Shape the Future of AI:
Machine Learning Engineer

Are you ready to take ownership of the full ML lifecycle and turn complex data into world-changing products? We are looking for a proactive Machine Learning Engineer to bridge the gap between research and production. If you are passionate about deploying deep learning models at scale and mentoring the next generation of innovators, your work here will directly transform how global industries operate.

Trimble is a global technology company that connects the physical and digital worlds, transforming the ways work gets done. With relentless innovation in precise positioning, modeling and data analytics, Trimble enables essential industries including construction, geospatial and transportation. The Trimble AECO segment provides digital construction solutions that increase precision and productivity for Architecture, Engineering, Construction, and Operations.

In this role, you ll be the technical architect of our machine learning future, moving beyond theory to deploy deep learning models that solve real-world industrial challenges  will have the unique opportunity to act as a visionary pragmatist—independently identifying and refining models that directly enhance the accuracy and reliability of our global product suite.

• Mastermind the ML Lifecycle: Spearhead the full journey of deep learning models from initial conceptualization to large-scale production deployment.

• Drive MLOps Excellence: Elevate our technical standards by implementing robust containerization, versioning, and monitoring practices to ensure model reliability.

• Bridge the Gap: Collaborate with domain experts to translate ambiguous business needs into crisp, actionable machine learning tasks and technical solutions.

• Innovate at the Frontier: Research and adapt breakthrough methods, such as foundation models and self-supervised learning, into our production environment.

• Empower the Team: Serve as a pivotal technical resource, providing informal guidance and sharing best practices to foster the growth of emerging talent.

• Bachelor s degree in Computer Science, Math, Engineering, Statistics, or a related quantitative field with 3-5 years of relevant experience, or a Master s degree with 1-3 years of relevant experience, demonstrating in-depth knowledge and practical application.

• In-depth conceptual and practical knowledge of machine learning and deep learning principles, including model selection, training, and evaluation methodologies.

• Proven track record as a Machine Learning Engineer or similar role, with evidence of production-ready ML work, including shipped projects, open-source contributions, or strong Git Hub portfolios.

• Fluency in Python and deep learning frameworks (PyTorch is a must, Tensor Flow is welcome), along with strong familiarity with Num Py, pandas, and scikit-learn.

• Experience with Linux and bash scripting.

• Solid grounding in data structures, data modeling, and software architecture, with an understanding of how code fits together and how to maintain its tidiness.

• Practical statistical sense, with the ability to run experiments with rigor, understand how to collect data, compare approaches, and avoid biased sampling or flawed metrics.

• Strong algorithmic and problem-solving skills, capable of tackling complex challenges with a structured approach and knowing when to iterate rapidly and deploy effective solutions.

• Experience with large-scale computing frameworks (e.g., Spark, Hadoop) and designing/implementing solutions for distributed systems.

• Contributions to open-source projects or academic publications in ML/AI, showcasing thought leadership.

• Experience with MLOps practices for seamless model deployment, monitoring, and lifecycle management, including tools like Azure Dev Ops, New Relic, and Azure Monitor for AI-driven infrastructure optimization and automated incident management.

• Familiarity with cloud-native ML services (e.g., Azure Cognitive Services, AWS Sage Maker) and their integration into broader architectures.

• Adaptable and curious mindset, eager to dig into…