Lead and apply advanced statistical and machine learning methods (causal inference, experimental design, anomaly detection) on large-scale ad data. Collaborate with product, engineering, and other data scientists to define metrics, build models, analyze trends, and communicate recommendations. Requires an advanced quantitative degree and several years of industry data science experience; experience with Spark/Hadoop/Hive/BigQuery, Python or R, ML libraries, and SQL.

Role involves building high-performance compiler and runtime components to map large-scale neural network inference workloads to NVIDIA platforms. Requires hands-on experience with compiler/runtime development (LLVM/MLIR), systems-level programming (C++/Rust), deep learning frameworks (TensorFlow/PyTorch/ONNX), and performance profiling/optimization. Minimum 5 years relevant experience and MS/PhD or equivalent experience.

Design, implement, and operate end-to-end production ML systems including large-scale data pipelines, model training, serving, monitoring, and automated retraining. Work cross-functionally to improve ranking, recommendation, search relevance, advertising systems, and LLM-powered products. Requires 3+ years building and deploying ML systems, strong software engineering skills (Python/Java/Go), experience with ML frameworks (PyTorch/TensorFlow), and familiarity with large-scale data and real-time systems.

Remote Senior Machine Learning Engineer based in the United States or Canada. Requires 5+ years in software engineering with strong backend experience (Go or Python), hands-on applied ML with large datasets (PyTorch, Scikit-learn), strong SQL and database familiarity, and end-to-end ML systems experience (feature pipelines, deployment, monitoring). Responsibilities include building data pipelines and backend services, developing and deploying production ML models for fraud detection, and collaborating on integration, security, and observability.

Hands-on senior data science position to design, prototype, and productionize machine learning and graph analytics solutions for fraud and risk. Responsibilities include client engagement, building metrics and dashboards, conducting EDA and cohort analyses, collaborating with engineering to scale models, and driving data-driven risk strategies. Remote-first role covering specified countries in Europe and North America; requires 7+ years experience.

Lead architecture and development of vehicle and sensor abstraction layers (VAL/SAL), design plugin frameworks and interfaces to sensors and actuators, and improve integration across vehicle and sensor configurations. Responsible for developing production-quality C++ code (including GPGPU/CUDA), validating through real-world and synthetic testing, following automotive safety/quality standards (e.g., MISRA), and managing teams. Requires MS (or equivalent), 12+ years relevant experience and 8+ years of management experience.

Design and implement ML models that scale to millions of requests per second, translate business requirements into technical specifications, evaluate and debug models, and provide technical mentorship. Role combines research, engineering, and deployment work in a fully remote, flexible-hours environment. Requires 5+ years professional applied ML experience and experience with real-time, incremental, and online learning.

The role leads the ML Security team responsible for bot, agent, and human fraud/abuse detection, owning execution of roadmaps, architectural and technical decisions, code reviews, mentoring, and delivery from data exploration through production deployment and monitoring. Required: strong ML/data science or engineering background with production ML experience, engineering management or tech lead experience, expertise in high-availability and distributed systems, Python, ML pipelines, MLOps, and strong cross-functional communication. Position is fully remote with flexible hours; all candidates will undergo pre-employment screening and identity verification in their country of residence.