Staff Software Engineer – Machine Learning

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Company Overview

A fast-growing technology organization is building a decentralized global mapping data network powered by thousands of contributors and edge devices. The network collects high-quality sensor data, including high-resolution imagery, GNSS, and IMU information, while performing advanced computer vision computations at the edge.

The organization processes large volumes of standardized data through sensor fusion, 3D reconstruction, machine learning, and human-in-the-loop annotation. Its data products provide programmatic access to high-resolution imagery, sensor data, and precisely extracted map features, supporting customers across enterprise technology, mapping, autonomous transportation, ridesharing, and real estate analytics.

The Computer Vision team works across a broad range of machine learning and computer vision challenges, including object detection, stereo depth mapping, feature detection and matching, 3D reconstruction, image classification, OCR, and general sensor fusion. The team focuses on transforming large volumes of standardized signals into accurate and actionable insights.

The organization is seeking a Staff Software Engineer – Machine Learning to help shape computer vision strategy across the full mapping technology stack, from hardware and edge computing through data processing and insights.

The successful candidate will balance cutting-edge machine learning research with practical production requirements, developing efficient and scalable ML solutions trained on large datasets. This role will also involve integrating machine learning systems into production environments, including edge devices and large-scale offline computing clusters.

Key Responsibilities

• Help shape the computer vision strategy across the complete mapping technology stack, from hardware to data insights.
• Balance state-of-the-art and emerging machine learning techniques with practical production requirements.
• Develop production-grade machine learning solutions using large-scale standardized datasets.
• Optimize machine learning systems for cost, performance, efficiency, and scalability.
• Integrate machine learning solutions into production software systems.
• Deploy ML solutions across edge computing environments and large offline compute clusters.
• Contribute to computer vision systems involving object detection, classification, tracking, localization, 3D reconstruction, and related technologies.
• Develop solutions that transform large volumes of sensor data into accurate and precise mapping insights.
• Collaborate with engineering and data teams to deliver reliable machine learning capabilities across the mapping platform.

Required Qualifications

• Demonstrated expertise in building machine learning solutions, including training and deploying models and integrating them into production software systems.
• Hands-on experience with image processing and computer vision, including object detection, classification, tracking, localization, 3D reconstruction, and vector embeddings.
• Hands-on experience with general machine learning and data mining techniques, including clustering, prediction, unsupervised methods, ensemble methods, and graph optimization.
• Hands-on experience with 3D reconstruction pipelines, including monocular or stereo approaches.
• Strong programming and applied mathematics skills, including linear algebra, statistics, and multivariate optimization.
• Strong software engineering fundamentals.
• Experience developing scalable and production-ready machine learning systems.
• Ability to work effectively across machine learning, software engineering, computer vision, and data processing disciplines.

Nice-to-Have Qualifications

• PhD in Computer Vision or a related field.
• Knowledge of distributed computing systems such as Hadoop and Spark.
• Experience with multiple machine learning frameworks and technologies, including PyTorch, TensorFlow, OpenVINO, and ONNX.
• Experience working with large-scale computer vision or sensor-fusion systems.
• Experience deploying machine learning models across edge and cloud-based environments.

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