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Design and deploy enterprise AI/ML solutions on the Snowflake native stack (Cortex, Model Registry), perform root cause analysis on complex distributed systems, mentor architects, and align technical roadmaps with business outcomes.
Builds and scales production ML systems for Warner Bros. Discovery’s media brands, focusing on identity resolution, audience intelligence, and content affinity. Owns end-to-end ML pipelines, feature stores, and agentic AI workflows on Databricks/Snowflake/AWS to drive advertising yield, personalization, and engagement.
Employee Applicant Privacy Notice Who we are: Shape a brighter financial future with us. Together with our members, we’re changing the way people think about and interact with personal finance. We’re a next-generation…
Build and run machine learning models that turn high-volume security telemetry into accurate, low-noise detections for SoFi’s SOC and fraud teams, using Python, SQL, Spark, and cloud data platforms.
Senior Staff Software Engineer on LinkedIn's AI Infrastructure team, responsible for designing and optimizing large-scale distributed training and serving systems for AI models (e.g., LLMs, recommendation engines), using frameworks like PyTorch, TensorFlow, Horovod, and DeepSpeed to scale up to hundreds of billions of parameters and high-throughput GPU inference.
About Our Company We are an innovative biomedical data company dedicated to helping healthcare institutions realize the full value of their biobanks. Through trusted, long-term partnerships, we are building a secure,…
A generalist data scientist applies ML to solve data problems (e.g., quality, classification, embeddings) and builds tools for analysts. Works independently on projects, embedded with product/data teams, and partners with engineers, analysts, and PMs.
Works on the Border Wait Time project, preparing traffic congestion datasets, developing ML models to predict traffic volumes, and deploying solutions with Google Vertex AI. Core technologies include Python, TensorFlow/PyTorch/scikit-learn, and Google Cloud Platform (Vertex AI, BigQuery, Cloud Storage).
Data Scientist/MLOps Engineer developing ML models, feature engineering, and maintaining data/training pipelines (batch and real-time). Deploy models as APIs using FastAPI and work with large data volumes using Spark and SQL.
Leads data science initiatives for global clients, designing ML models, deploying solutions on AWS, and mentoring teams to deliver measurable business value through end-to-end analytics.
Day-to-day tasks include modeling/visualizing data, developing ML models, hypothesis testing, data collection, feature engineering (time series), model selection, A/B testing, and supporting IT/business solutions. Core technologies: Python, SQL.
Designs and deploys GenAI models and systems for Databricks’ AI-powered products like Assistant and AI/BI Genie, focusing on LLM quality and scalable ML pipelines.
Senior role troubleshooting and optimizing GenAI/ML workloads on Databricks, advising customers on AI agents, vector search, and model serving while collaborating with engineering teams.
Designs, builds, and maintains scalable data pipelines and lakehouse architectures to enable analytics, reporting, and ML across BC Transit’s operations. Focuses on modern data engineering practices, automation, and mentoring engineers to improve organizational data capabilities.
Design and implement AI solutions for RBC's banking operations, building scalable data and ML pipelines, integrating LLM applications, and ensuring security and compliance.
Build AI/ML models and data pipelines for a microfluidic blood-analysis device, turning raw sensor data into clinical diagnostics and deployable prototypes.
Reporting to Data and Analytics Lead, the successful candidate will be responsible for the following: Data Management & Transformation Designing and developing new data pipelines and managing existing data…
Develop and deploy AI/ML models—deep learning, computer vision, and multimodal AI—using TensorFlow or PyTorch, collaborating with cross-functional teams in Indonesia.
Transforms raw telematics data into production-ready features for pricing/underwriting using SQL and dbt, builds scalable data models/pipelines, and collaborates with cross-functional teams.
Optimizes ML-driven lending systems by monitoring, retraining, and developing new models for automated decision-making, integrating data sources, and refining pipelines for accuracy and scalability.
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