Senior Software Engineer - ML&Data Science team

<p><strong>Our Team:</strong></p> <p>Our ML teams are part of our core Data Science and Machine Learning group and consist of Online and Offline ML teams.</p> <p>The Online ML team is responsible for building and operating low-latency and data-intensive systems such as a feature store, feature extraction, ML model serving, and versioning systems. </p> <p>The Offline ML team is responsible for ML model release process, ML pipelines, model training, and validation.</p> <p><strong>Tech stack:</strong></p> <p>- Java</p> <p>- GCP</p> <p>- VertexAI</p> <p>- Flink</p> <p>- Dataflow</p> <p>- Dataproc</p> <p>- Airflow</p> <p><strong>Opportunities for you:</strong></p> <ul> <li>Professional growth: quarterly Growth Cycles instead of performance review;</li> <li>Experience: knowledge sharing through biweekly Tech Talks sessions. You will learn how to build projects that handle petabytes of data and have small latency and high fault tolerance;</li> <li>Business trips and the annual Sift Summit, in 2022, Summit took place in California;</li> <li>Remote work approach: you can choose where you work better.</li> </ul> <p><strong>What would make you a strong fit:</strong></p> <ul> <li>7+ years of professional software big data development experience;</li> <li>Experience building highly available low-latency systems using Java, Scala, or other object-oriented languages;</li> <li>Knowledge of GCP or AWS cloud stack for web services and big data processing;</li> <li>Basic knowledge of MLOps on model release/training/monitoring;</li> <li>Conceptual knowledge of ML techniques;</li> <li>B.S. in Computer Science (or related technical discipline), or related practical experience.</li> </ul> <h4><strong>Bonus points:</strong></h4> <ul> <li>Experience working with large datasets and data processing technologies for both stream and batch processing, such as Apache Spark, Apache Beam, Flink, and MapReduce;</li> <li>Experience solving problems with production systems, and building solutions and automation to prevent them from reoccurring;</li> <li>Familiarity with practical challenges in ML systems such as feature extraction and definition, data validation, training, monitoring, and management of features and models;</li> <li>Practical knowledge of how to build end-to-end ML workflows;</li> <li>Experience with building an ML feature store for batch and real-time aggregation/serving.</li> </ul> <p><strong>What you’ll do:</strong></p> <ul> <li>Building and operating low-latency and data-intensive systems;</li> <li>Designing, implementing, and operating large-scale distributed systems;</li> <li>Execute and improve the ML model release process;</li> <li>Collaborate with US-based ML teams and work with them in close partnership on core components of Sift products.</li> </ul> <p><strong>A little about us:</strong></p> <p>Sift is the leading innovator in Digital Trust &amp; Safety.  Hundreds of disruptive, forward-thinking companies like Doordash, Binance, and Twitter trust Sift to deliver an outstanding customer experience while preventing fraud and abuse.</p> <p>Sift is a series E company with a valuation of $1.7 billion as a unicorn. In 2021, Sift acquired 2 startups: Chargeback and Keyless to extend the company's product portfolio. Sift was nominated as the Best Employer in 2020 in Seattle.</p> <p>Sift is a big data ML-based platform that processes 70B API requests per month, processes 1PB of data, and tens of thousands of transactions per second.</p> <p><strong>Let’s Build It Together</strong></p> <p>At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community – ultimately using this empowerment and authenticity to build trust and create a safer Internet.</p> <p><em>This document provides transparency around the way in which Sift handles personal data of job applicants: <a href="https://sift.com/recruitment-privacy">https://sift.com/recruitment-privacy</a></em></p>