Software Engineer (Computer Vision and Machine Learning)

About the Role
RPMGlobal (RPM) is seeking a Computer Vision and machine learning Software Engineer to join our development team and develop algorithms that transform imagery, LiDAR, point clouds, and other sensor data into accurate and actionable information about the physical world.
You will work on challenging problems across Computer Vision, machine learning, and 3D geometry, building capabilities used to understand large-scale mining and industrial environments. Your work will go beyond research and prototypes, bringing algorithms into reliable production systems running across cloud and edge environments.
This role is ideal for an experienced engineer who enjoys combining algorithms, machine learning, and strong software engineering to solve complex real-world problems.
This is a full-time role, and while it is remote, candidates must be based in Guadalajara.
To be considered for this role, please submit your application in English.
Key Responsibilities
Research, design and implement Computer Vision and machine learning algorithms using C++ and Python.
Work on 3D vision problems, including SLAM, Structure from Motion, etc.
Train, evaluate, and optimise Machine Learning and Deep Learning models, from dataset preparation/experimentation through production deployment.
Optimise algorithms for accuracy, performance, and scalability across cloud, on-premise, and edge environments.
Collaborate with development teams and product managers to integrate Computer Vision and Machine Learning capabilities into customer-facing products.
Contribute to architecture, code reviews, automated testing, and development standards.
About You
Relevant tertiary education, such as a Master of Science or PhD in computer science, Computer Vision, robotics, machine learning, or a related technical field, or equivalent professional experience.
Experience with LiDAR, point clouds, photogrammetry, SLAM, Structure from Motion, pose estimation, or 3D reconstruction.
Strong proficiency in C++ and Python, including performance optimisation, memory management, data structures, and algorithms.
Exposure to working with libraries and technologies such as OpenCV, PCL, CUDA, OpenCL, or OpenMP.
Experience developing and training Deep Learning models using frameworks such as PyTorch or TensorFlow.
Ability to optimise Computer Vision or machine learning workloads for GPU and high-performance computing environments.
Experience with Docker, CI/CD, and cloud platforms such as AWS, Azure, or GCP.
Ability to work with large datasets and design robust evaluation methodologies, metrics, and validation workflows.
Strong software engineering practices, including code reviews, version control, automated testing, debugging, and profiling.
Fluency in Spanish and English.
Legally entitled to work in Mexico.
Highly Regarded Qualifications and Experience
Experience with geospatial applications, coordinate systems, spatial analysis, or large-scale mapping workflows.
Experience with autonomous systems, robotics, drones, or multi-sensor platforms.
Experience in mining, construction, autonomous systems, robotics, or other industrial environments.
About RPM
RPMGlobal is the mining industry’s leading enterprise technology and training provider and has been at the forefront of change and innovation in the mining industry for over 50 years.​​
We advance the mining industry towards a safer, more efficient, and more sustainable future through the delivery of innovative software solutions and deep domain mining expertise. Guided by our underlying principles of mining expertise, innovation, and sustainability, we strive to create safer and more efficient operations for our mining customers.
Our focus on providing advanced mining enterprise technology and training across the mining lifecycle enables us to guide customers through the industry's current and emerging challenges worldwide. This includes helping them meet the shift in social norms and consumer and investor ESG-related expectations.
Please, no agencies or recruiters.

Employment type
Full-Time

Work arrangement
Yes