The Data Platform team works closely with all teams and cross-functional partners (including product, engineering, and data analysts) to build a foundational data stack powering business analytics.

We are looking for someone to play a mission-critical role in designing and building the machine models for risk, fraud, recommendation, and personalization that powers Bolt. This should be someone with experience, creativity, and passion for producing world-class technology. Companies and consumers alike will rely heavily on what you build, and you’ll have a ton of trust and responsibility. If challenges excite you, and you’re ready for a large one, let us know.

What you will be doing: Build production ready machine learning models; your models will be the engine that powers online commerce through Bolt Establish reusable frameworks to streamline model building, deployment and monitoring, incorporating comprehensive monitoring, logging, tracing, and alerting mechanisms Conduct data analysis to determine which policies we adopt and help inform strategic growth Build machine learning infrastructure, data pipelines and production ready services to serve live traffic Collaborate with Product Managers to ensure proper tracking of algorithmic performance KPIs and prioritize performance improvements based on effort and impact Work with other teams at Bolt to engineer new features for models or new product features that help improve Bolt business What would set you up for success: Minimum of three years’ post-secondary education or relevant work experience, along with minimum of two years’ software development experience with Python and SQL Minimum of two years’ experience using PyTorch, Tensorflow, Spark ML for building pipelines to deploy NLP and deep learning models into production in a cloud (AWS, GCP, Azure) environment Thorough understanding of machine learning fundamentals and methodologies Experience building and deploying machine learning models in an applied setting Strong understanding of how to build scalable ML systems supporting online and offline applications Familiarity with best practices of lifecycle management for ML models in industry What would set you apart: Retail and/or Martech experience in building large-scale personalization and recommendation systems in a consumer-based setting. Experience with big data tools like Spark, Kafka, BigQuery, Dataflow, Apache Beam, Pubsub, Cloud Functions, EMR, S3, Glue, Kinesis Firehose, Lambda; a Linux environment Industry experience in building large-scale recommendation systems in a consumer-based setting.

Estimated cash compensation: $180k-240k plus equity DOE