Machine Learning Engineer
- Employment Type
- Full time
- Compensation
- $140K – $185K
Property Shield is an Atlanta-based data security firm focused on building the fraud prevention infrastructure for residential real estate. Since our start in 2023 we’ve helped save US renters over $1B in potential losses to rental scams.
We are seeking a highly skilled and motivated Machine Learning (ML) Engineer with 2-3 years of experience to join our team and help lead core initiatives for machine learning at our company. The ideal candidate has proven experience building and deploying ML applications at scale, expertise in Computer Vision + Image Processing, and a solid understanding of data security and predictive analytics.
Key Responsibilities
- Lead the design, development, and maintenance of ML applications at scale
- Ensure scalability, performance, and efficiency of ML applications
- Design and implement advanced Computer Vision techniques for similarity, feature extraction, classification, and object detection.
- Collaborate with cross-functional teams to identify and implement ML solutions for business problems
- Develop and implement best practices for ML model development, deployment, and monitoring
- Stay current with the latest advancements in ML and AI, and integrate new technologies when appropriate
Required Qualifications
- 2-3 years of experience deploying and maintaining machine learning applications at scale (ML Ops)
- Proven ability to scale products and manage real-time workloads
- Bachelor's or Master's degree in Computer Science, Data Science, or a related field
- Strong proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or scikit-learn
- Familiarity with cloud platforms, particularly AWS, and containerization technologies like Docker
- Experience with predictive analytics and implementing forecasting models
- Excellent problem-solving skills and attention to detail
Preferred Qualifications
- Familiarity with cybersecurity concepts and fraud detection techniques
- Experience working in a startup environment
- Contributions to open-source ML projects
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