About The Role

Machine Learning Engineer

ABOUT WIND RIVER

Wind River is a global leader in delivering software for mission-critical intelligent systems. For more than four decades, the company has been an innovator and pioneer, powering billions of systems that require the highest levels of security, safety, and reliability.

Wind River helps customers across automotive, aerospace, defense, industrial, medical, and telecommunications industries solve complex technology challenges on their journey toward the new intelligent machine economy. The company’s software powers generation after generation of the safest, most secure systems in the world. Examples include playing a key role in NASA space missions such as Artemis I, the James Webb Space Telescope, and multiple Mars rovers. We’ve achieved recent 5G milestones including the world’s first successful 5G data session with Verizon and building one of the largest Open RAN networks in the world with Vodafone.

The company has received industry recognition for its technology innovation and leadership, and for its workplace culture, including global Great Place to Work certification and being named a “Top Workplace” for ten consecutive years. If you want to be part of a unique culture where the lived experience is based on our cultural attributes of growth mindset, customer-focus, and diversity, equity, inclusion & belonging, come join us and help advance the future software defined world.

Role overview for Machine Learning & Computer Vision Engineer – 3D Reconstruction & Human Pose Understanding related position.

YOUR ROLE

As a Machine Learning Engineer on our team, you’ll

Develop, train, evaluate, and optimize machine learning and deep learning models for a wide range of computer vision tasks, including human body pose estimation, volumetric modeling, object detection, semantic segmentation, and classification.
Implement classical and modern computer vision algorithms such as feature detection/extraction, depth matching, 3D scene reconstruction, and novel-view generation.
Apply strong Python development skills to build high-quality, maitainable, and efficient code for ML training pipelines, data processing, visualization, and prototyping.
Work with 3D data structures including point clouds, meshes, and volumetric representations; develop tools for point cloud computation, registration, transformation, and visualization.
Perform mesh fitting and geometry aware model integration into the processing pipelines.
Rapidly prototype new algorithmic concepts, ML models, or geometry based methods for feasibility evaluation, benchmarking, or customer demonstrations.
Support data collection campaigns, ensuring high-quality recordings and validating that collected data meet ML training and evaluation requirements.
Contribute to the productization of developed algorithms by helping integrate them into automated pipelines (e.g., Dockerization, CI/CD workflows, reproducible ML environments).
Collaborate closely with R&D engineers, 3D reconstruction specialists, cloud pipeline engineers, and data operations teams to ensure seamless integration of ML algorithms within the end-to-end system.
Document algorithms, experiments, design decisions, and technical findings thoroughly to support team knowledge and future development.

HOW YOU WILL CONTRIBUTE

Key skills and competencies for succeeding in this role are

Master’s degree or PhD in computer vision, machine learning, robotics, computer science, or a related field.
Strong experience in training and evaluating ML models, ideally in tasks like pose estimation, segmentation, object detection, or 3D/volumetric modeling.
Solid background in classical computer vision, photogrammetry, and multi-view geometry.
Strong Python programming skills and experience with ML frameworks such as PyTorch or TensorFlow.
Experience working with 3D data formats (point clouds, meshes) and corresponding processing/visualization tools.
Knowledge of depth estimation algorithms (stereo, multi-view, geometric, or learning based).
Strong analytical skills and ability to independently contribute to experiments and model improvements.
Excellent communication skills, strong team mindset, and willingness to support cross functional efforts.
Fluent in English.

Preferred Qualifications

Experience with C++ for high-performance algorithmic implementations.
Familiarity with novel-view generation methods, differentiable rendering, or neural rendering techniques (e.g., NeRFstyle approaches).
Understanding of Docker, CI/CD pipelines, and automation for ML workflows.
Experience with pose estimation datasets, 3D human models, or mesh based learning.
Prior involvement in data collection efforts or real-world ML experimentation environments.
Background in signal processing, sensor fusion, or geometry driven ML techniques.