Cargill
Bangalore
Food Production
Job Purpose and Impact
Machine Learning Software Engineer will research, design, implement, optimize and deploy computer vision models that advance the state of the art in Ag and Food to help deliver significant value to our businesses and functions. Key Accountabilities
Help compile and prioritize enhancements and defect resolutions to applications, implementing changes.
Conduct technical software testing and debug systems and software applications, as needed.
Perform basic and less complex programming, coding and documentation of systems and applications software.
Collaborate with internal and external partners to understand and evaluate business requirements.
Integrate software components into a fully functional software system.
Independently solve moderately complex issues with minimal supervision, while. escalating more complex issues to appropriate staff.
Customize latest available models in computer vision in domains like classification, object detection and segmentation and integrate them in our product. You would be responsible for the end-to-end solution, from offline experiments to production level code that uses these models.
Apply the latest advances in deep learning and machine learning to increase our current models’ performance.
Contribute to the product road map and planning.
Develop and maintain software programs, algorithms, dashboards, information tools, and queries to clean, model, integrate and evaluate data sets.
Optimize pipelines for data intake, validation, and mining as well as modeling and visualization by applying best practices to the engineering of large data sets.
Provide findings and analysis to take informed business decisions.
Other duties as assigned Qualifications
Minimum Qualifications
Bachelor’s degree in a related field ( data science, computer science, math ) or practical software engineering experience in related fields.
Strong software engineering practices and is very comfortable with Python programming, debugging/profiling, and version control.
Experience in Machine Learning and Deep Learning theory and frameworks like Tensorflow, PyTorch, etc to perform visual recognition tasks, such as segmentation, detection and classification.
Experience with data science tools including Python scripting, numpy, scipy, matplotlib, scikit-learn, jupyter notebooks, bash scripting, Linux environment.
Experience in collecting and manipulating structured and unstructured data from multiple data systems (on-premises, cloud-based data sources, APIs, etc) Preferred Qualifications
Experience working with product teams.
Proven ability to deliver end-to-end solutions, including data preparation, training, and production deployment.
Ability to read and implement related academic literature and experience in applying state-of-the-art deep learning models to computer vision.
Ability to diagnose and troubleshoot complex analytic implementation issues
Ability to prioritizewe are consistently working on multiple projects at any given time, and comfortable working with ambiguous requirements.
Understands the related computer systems concepts (CPU/GPU interactions/transfers, latency/throughput bottlenecks during training of neural networks, CUDA, pipelining/multiprocessing, etc).
Experience developing production applications for the edge/IoT is a strong plus
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