Who Are You
We seek a skilled and highly motivated Senior Software Engineer to join our dynamic and growing ML Engineering team. As a Senior Software Engineer for ML Engineering, you will be part of the team that builds platforms that empower fellow engineers and Data Scientists to create market-leading fraud prevention products. We want you to help us scale our business, make data-driven decisions, and contribute to our overall ML and data strategy. The ideal candidate must:
- Balance multiple perspectives, disagree, and commit when necessary to move key company decisions and critical priorities forward.
- Ability to work independently in a dynamic environment and proactively approach problem-solving.
- Be committed to driving positive business outcomes through expert data handling and analysis.
- Be an example for fellow engineers by showcasing customer empathy, creativity, curiosity, and tenacity.
- Have strong analytical and problem-solving skills, with the ability to innovate and adapt to fast-paced environments.
What You'll Do
- Modernize Signifyd’s Machine Learning (ML) Platform to scale for resiliency, performance, and operational excellence, working closely with Engineering and Data Science teams across Signifyd’s R&D group.
- Work alongside ML Engineers, Data Scientists, and other Software Engineers to develop innovative big data processing solutions for scaling our core product for eCommerce fraud prevention.
- Contribute to all processes of the ML lifecycle: data collection, annotation, modeling, evaluation, deployment, and monitoring.
- Write production-quality code for ML models as online services and APIs.
- Implement data and ML processing solutions for offline, batch, and real-time use cases.
- Mentor and coach fellow engineers on the team, fostering an environment of growth and continuous improvement.
- Identify and address gaps in team capabilities and processes to enhance team efficiency and success.
- Automate monitoring of model performance and user behavior.
- Take ownership of solutions from analysis to implementation.
- Influence the tooling, frameworks, and ML practices with the ML teams.
- Stay updated with the latest in Data Science and ML tooling & communities
- Present complex analyses clearly and concisely.
What You'll Need
- Ideally has 3-7 years of experience in data/ML engineering. Has experience navigating the challenges of working with large-scale data processing systems.
- Experience in contributing toward or building low-latency, high-availability data stores for real-time or near-real-time data processing with programming languages such as Python, Scala, Java, or JavaScript/TypeScript, as well as data retrieval using SQL and NoSQL.
- Hands-on expertise in data technologies with proficiency in Spark, Airflow, Databricks, AWS services (S3, EMR, SQS, Kinesis, etc.), and Kafka. Understand the trade-offs of various architectural approaches and recommend solutions suited to our needs.
- Experience in programming languages such as Java, Python, or Scala and experience understanding Cloud infrastructure environments including Kubernetes and Serverless.
- Working knowledge of ML algorithms, clustering algorithms, and binary classifiers (such as XGBoost)
- Solid knowledge of ML principles applied to recommendation systems.
- Familiarity with relational databases (Postgres, MySQL, etc).
- Experience using feature stores is a plus: homegrown solutions or commercial and open-source products like Tecton and Chronon.