Senior Software Engineer​/Machine Learning Engineer; Device Identification

Who we are:

We are a leader in fraud prevention and AML compliance. Our platform uses device intelligence, behavior biometrics, machine learning, and AI to stop fraud before it happens. Today, over 300 banks, retailers, and fintechs worldwide use Sardine to stop identity fraud, payment fraud, account takeovers, and social engineering scams. We have raised $75M from world-class investors including Andreessen Horowitz, Visa, Experian, FIS, and Google Ventures.

Our culture:

• We have hubs in the Bay Area, NYC, Austin, and Toronto. However, we have a remote-first work culture. #Work From Anywhere

• We hire talented, self-motivated people and get out of their way

• We value performance and not hours worked. We believe you shouldn’t have to miss your family dinner, your kid’s school play, or doctor’s appointments for the sake of adhering to an arbitrary work schedule.

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.

• Education :
  Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

• Experience :

• Technical Skills :

• Soft Skills :

• Machine Learning :
  Experience with machine learning algorithms and techniques. (python/notebooks/etc)

• Security Expertise :
  Understanding of cybersecurity principles, especially related to device identification and fraud prevention.

• Cloud Technologies :
  Experience with cloud platforms such as AWS, Google Cloud, or Azure.

• Dev Ops Skills :
  Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines.

• SQL Proficiency :
  Strong SQL skills to query, analyze, and validate data effectively, especially for large-scale datasets.

• Python and Jupyter Notebooks :
  Experience with Python for data analysis and machine learning model development, with familiarity in using Jupyter Notebooks for prototyping and collaboration.

• Additional Considerations :
  Knowledge of JavaScript and familiarity with modern browser APIs, especially in the context of high-entropy data collection for device fingerprinting.

Compensation: Base pay range of $160,000 - $190,000 + Series B equity with tremendous upside potential + Attractive benefits

Benefits we offer:

• Generous compensation in cash and equity

• Early exercise for…