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Staff Software Engineer - Machine Learning

Staff Software Engineer - Machine Learning

Hivemapper

San Francisco, CA • On-site

Full-time

Posted 7 days ago

Job description

Job Summary:
Hivemapper is a decentralized global mapping data network built by tens of thousands of contributors. The Staff Software Engineer - Machine Learning will help shape the computer vision strategy, produce production-grade ML solutions, and integrate these solutions with production systems.
Responsibilities:
• Help shape the CV strategy touching the full mapping stack, all the way from hardware to data insights
• Balance the state of the art and bleeding edge with practicality; produce production-grade ML solutions trained on a huge corpus of standardized data that are efficient w.r.t cost and performance
• Integrate ML solutions with our production systems; at the edge and in large offline clusters
Qualifications:
Required:
• Demonstrated expertise in building ML solutions, including training and deploying models, as well as integrating them into production software systems
• Hands-on experience with Image Processing and Computer Vision: Object Detection, Classification, Tracking, Localization, 3D Reconstruction, Vector embeddings, etc.
• Hands-on experience with general ML and Data Mining: Clustering, Predictions, Unsupervised Methods, Ensemble Methods, Graph Optimization, etc.
• Hands on experience with 3D reconstruction pipelines (either monocular or stereo)
• Strong programming and applied math skills (linear algebra, statistics, multivariate optimization)
• Strong software engineering fundamentals
Preferred:
• PhD in Computer Vision or related field
• Knowledge of distributed compute systems like Hadoop/Spark
• Experience with a variety of different ML frameworks (PyTorch, TensorFlow, OpenVINO, ONNX, etc.)
Company:
Hivemapper is a community-owned mapping network that creates unprecedented coverage, freshness, and quality. Founded in 2015, the company is headquartered in San Francisco, USA, with a team of 11-50 employees. The company is currently Early Stage.

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