Staff SW Engineer, Machine Learning
New
B
BlackSkySpace intelligence
Remote, USAFull-TimeStaff
Salary150,000 - 180,000 USD per year
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Job Details
- Experience
- At least eight years
- Required Skills
- PythonKerasMachine LearningNumpyPyTorchPandasTensorflowDeep Learningscikit-learnComputer Vision
Requirements
- At least eight years of hands-on experience as a machine learning engineer or data scientist.
- Bachelor’s Degree or higher in computer science, mathematics, physics, statistics, or another computational field.
- Extensive experience developing machine learning based software solutions (Python 3, PyTorch, Tensorflow, Keras, or scikit-learn).
- Working knowledge of a wide range of machine learning concepts including supervised and unsupervised deep learning methods.
- Experience performing research in both groups and as a solo effort.
- History of implementing algorithms directly from research papers.
- Experience conducting literature review and applying concepts to programs or products.
- Strong ability to communicate concepts and analytical results with customers, management, and the technical team.
- Hands-on experience working with large data sets including data cleansing/transformation, statistical analyses, and visualization (Pandas, NumPy).
Responsibilities
- Design and implement solutions for internal and external customers that exploit traditional machine learning and novel deep learning for next-generation satellite imagery analytics.
- Plan and conduct research projects related to computer vision, time series analysis, content curation, probabilistic modeling, machine learning, predictive analytics, and geometric modeling.
- Develop algorithms, models, and analytical tools for solving domain specific business problems.
- Implement production quality analytics and models into the SpectraAI codebase (Python).
- Collaborate with management and technical team on product strategy.
- Collaborate with infrastructure developers and machine learning quality engineers to build robust analytics for production use cases.
- Independently design and conduct experiments, tests hypothesis, implement model and loss function code, train models, and interpret experiment results following a machine learning process based on high level project objectives.
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