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Senior Machine Learning Engineer

Sardine

Saas

51-200

San Francisco, CA, USA

About the job

Overview:

Who we are:
We are a leader in financial crime prevention. Using unparalleled device intelligence and behavior biometrics, we apply machine learning to detect and stop fraud before it happens. The platform includes tools for identity verification, fraud prevention and investigation, AML monitoring, and case management. Over 250 companies use Sardine to prevent fake account creation, social engineering scams, account takeovers, bot attacks, payment fraud, and money laundering. We raised $75M, led by Andreessen Horowitz Growth, XYZ Capital, Google Ventures, Visa, Activant Capital, Experian, and ING Ventures.
Our culture:

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

Location:

Remote - Canada or United States
From Home / Beach / Mountain / Cafe / Anywhere!
We are a remote-first company with a globally distributed team. You can find your productive zone and work from there.

About the role:
As a Machine Learning Engineer, you will play a pivotal role in designing, developing, and implementing cutting-edge machine learning solutions to mitigate fraud and risk, ensuring optimal outcomes for both our company and clients. Collaborating closely with cross-functional teams, you will contribute to the development of robust machine-learning infrastructure and feature engineering capabilities.
You will:

Design, develop, and implement machine learning models and algorithms utilizing Python and SQL.
Enhance backend systems for feature processing and model serving, optimizing efficiency and reliability.
Engage with large-scale data sets, data pipelines, and data warehousing tools to extract meaningful insights.
Collaborate with data scientists, software engineers, data analysts, account managers, and product managers to identify business challenges, devise solutions, and iteratively enhance machine learning models.
Establish and fortify ML observability to ensure machine learning models' steadfast performance and reliability in production.
Build scalable and efficient machine learning infrastructure utilizing advanced tools such as Vertex AI, Apache Beam, and Kubeflow.
Develop software systems and libraries to bolster machine learning applications and streamline integration.
Conduct experiments, execute statistical analyses, and assess model performance to optimize accuracy, reliability, and speed.

An ideal candidate has:

5+ years of hands-on experience in Machine Learning or related roles within Fraud, Risk, Compliance, Payments, or FinTech domains.
Extensive knowledge and educational background in computer science, machine learning, and statistics.
Strong programming proficiency in Python and SQL, coupled with hands-on experience in backend development.
Proficiency in data warehousing, data pipelines, and ETL tools, with a proven track record of managing the machine learning lifecycle.
Experience with cloud computing platforms such as GCP, AWS, or Azure.
Familiarity with Kubernetes or Docker for efficient containerization.
Proven track record of collaboration with data scientists to build, deploy, and refine machine learning models.
Prior experience in building ML observability to uphold the performance and reliability of machine learning models in production.