> Markdown version of [/jobs/ext/2706823-staff-software-engineer-machine-learning](https://www.wearedevelopers.com/jobs/ext/2706823-staff-software-engineer-machine-learning). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Software Engineer - Machine Learning - **Company:** Hivemapper Inc. - **Location:** San Francisco, CA, United States - **Contract:** Permanent contract - **Skills:** Computer Vision, Cluster Analysis, Data Mining, Apache Hadoop, Machine Learning, Object Detection, Tensorflow, Pytorch, Apache Spark, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/staff-software-engineer-machine-learning-hivemapper-3148681 ## About the Role * 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 Nice to haves * 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.) ## Description * 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 ## Related Videos - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) - [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Top 6 Hackathons for Developers in 2023](https://www.wearedevelopers.com/magazine/263-top-6-hackathons-for-developers-in-2023) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)