Machine Learning Engineer - Device Identification

Salary: $180K - $220K Employment Type: Full-Time Location Type: Remote

Who We Are

Sardine is a leader in fraud prevention and AML compliance. Our platform leverages device intelligence, behavior biometrics, machine learning, and AI to proactively stop fraud. We serve over 300 banks, retailers, and fintechs globally, helping them combat identity fraud, payment fraud, account takeovers, and social engineering scams. We have secured $145M in funding from prominent investors including Andreessen Horowitz, Activant, Visa, Experian, FIS, and Google Ventures.

Our Culture

While we have hubs in the Bay Area, NYC, Austin, and Toronto, we operate with a remote-first work culture, embracing a #WorkFromAnywhere philosophy. We value talented, self-motivated individuals who demonstrate extreme ownership and a high growth orientation. Our focus is on performance, not hours worked, as we believe in work-life balance and accommodating personal commitments.

About the Role

We are seeking a highly skilled Machine Learning Engineer to spearhead the development of our device identification and fingerprinting systems. You will collaborate with cross-functional teams to gather and process high-entropy signals from our frontend SDKs, enhance our backend systems, and refine the accuracy and reliability of our device fingerprinting methodologies.

Key Responsibilities

• Backend Development: Design, develop, and maintain backend services using Go (Golang) for processing and analyzing device data.
• Data Collection Optimization: Collaborate with frontend engineers to improve data collection methodologies using JavaScript and modern browser technologies.
• Device Fingerprinting: Implement and enhance algorithms for device identification, utilizing high-entropy signals and probabilistic matching techniques.
• Data Analysis: Process large datasets to derive insights and improve matching accuracy.
• Browser and Technology Monitoring: Stay current with evolving browser behaviors, APIs, and security features impacting data collection and fingerprinting.
• Machine Learning Integration: Apply machine learning models to bolster device recognition and manage uncertainty.
• Security and Compliance: Ensure all systems and processes adhere to relevant privacy laws and industry best practices.
• Performance Optimization: Identify and resolve performance bottlenecks to ensure scalability and reliability.
• Documentation and Mentorship: Document system designs and processes, and mentor junior team members, fostering best practices.

Required Qualifications

• Experience:
  • 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.
• Education:
  • Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• Technical Skills:
  • 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 for device identification.
  • 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.
• Soft Skills:
  • Strong problem-solving abilities and analytical thinking.
  • Excellent written and verbal communication skills.
  • Ability to work effectively in a team environment.
  • Self-motivated with a passion for continuous learning and improvement.

Preferred Qualifications

• Security Expertise: Understanding of cybersecurity principles, particularly in device identification and fraud prevention.
• Cloud Technologies: Experience with cloud platforms such as AWS, Google Cloud, or Azure.
• DevOps Skills: Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines.
• SQL Proficiency: Strong experience with SQL.