SiftSan Francisco, CABuild and deploy large-scale online ML models and end-to-end pipelines for real-time fraud detection at Sift, working on automated training systems that process over 1T events. Requires 4+ years production ML experience, strong Python/Java/Scala skills, and distributed systems expertise with Spark, Databricks, and GCP.
Model Development & Refinement: Design, build, and deploy online machine learning models (including ensemble methods, deep learning, transformer architectures and graph-based models) to catch evolving fraud vectors in real time.
Feature Engineering at Scale: Engineer high-frequency time-series features from over 1 trillion behavioral events, optimizing for low-latency signal extraction and pattern recognition.
Production MLOps: Maintain and enhance our automated model training and deployment infrastructure, ensuring frictionless continuous integration and continuous deployment (CI/CD) of newly trained models.
System Optimization: Write high-performance code to minimize scoring latency at runtime, ensuring our core ML services scale seamlessly across distributed databases.
Collaborative Innovation: Work cross-functionally with Core Infrastructure, Product Management, and Data Science teams to translate business-level fraud patterns into robust algorithmic solutions.
Develops and optimizes transformer-based vision-language foundation models for physical security, owning full-cycle training, fine-tuning, compression, and deployment for real-time inference on images, videos, and text. Requires PhD/Master's in CS/EE, hands-on ML expertise with PyTorch/TensorFlow, Transformers, and ViTs.
As a Perception Autonomy Engineer, you will develop, integrate, and maintain real-time sensor software solutions for autonomous vehicles. This involves designing and deploying AI/ML sensor algorithms, creating interfacing software for sensor control, and collaborating with cross-functional teams for seamless deployment and testing.
Build and maintain Lyft's ETA prediction system using ML models for ride matching, pricing, and user experience. Requires 3+ years ML experience, production code for high-scale low-latency systems, and tradeoff decisions on accuracy vs performance.
Founding ML Engineer building production ML systems for governance, security, and agentic platform capabilities at Docker. Requires 5+ years applied ML experience shipping systems and 4+ years backend/infra engineering.
Designs, develops, and optimizes GenAI inference engine for LLMs, focusing on performance, scalability, and efficiency across the full stack. Requires 3+ years in performance-critical systems, CUDA/GPU expertise, and ML inference knowledge.