Machine Learning Engineer, Sardine

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field
• Minimum of 5 years of professional software engineering experience
• At least 3 years of experience in backend development, preferably with Go or a similar language
• Proficiency in Go (Golang) or strong experience in another backend language with a willingness to learn Go
• Experience with data processing frameworks and handling large-scale datasets
• Experience with machine learning techniques, statistical analysis, or probabilistic modeling to improve device identification reliability and accuracy. Familiarity with Python-based data science tools and libraries (e.g., NumPy, pandas, scikit-learn) is a plus
• Familiarity with relational and non-relational databases
• Strong problem-solving abilities and analytical thinking
• Excellent communication skills, both written and verbal
• Ability to work collaboratively in a team environment
• Self-motivated with a passion for continuous learning and improvement

Desirable

• We are seeking a highly skilled Senior Software Engineer to lead the development of our device identification and fingerprinting systems
• In this role, you will work closely with cross-functional teams to collect and process high-entropy signals from our frontend SDKs, enhance our backend systems, and improve the accuracy and reliability of our device fingerprinting methods
• Backend Development: Design, develop, and maintain backend services using Go (Golang) to process and analyze device data
• Data Collection Optimization: Collaborate with frontend engineers to refine data collection methodologies using JavaScript and modern browser technologies and
• Device Fingerprinting: Implement and improve algorithms for device identification using high-entropy signals and probabilistic matching techniques
• Data Analysis: Handle large datasets to extract insights and improve matching accuracy
• Browser and Technology Monitoring: Stay up-to-date with changes in browser behaviors, APIs, and security features that may impact data collection and fingerprinting methods
• Machine Learning Integration: Apply machine learning models where appropriate to enhance device recognition and handle uncertainty
• Security and Compliance: Ensure all systems and processes comply with relevant privacy laws and industry best practices
• Performance Optimization: Identify bottlenecks and optimize system performance for scalability and reliability
• Documentation and Mentorship: Document system designs and processes. Mentor junior team members and promote best practices within the team