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Lead a machine learning team building foundation models and interpretable ML approaches (variational inference, causal modeling, transformers, diffusion) applied to single-cell biological data to understand Alzheimer's disease mechanisms at Arc Institute.
Build and improve a community-feed recommendation system for a stock-trading app used by 4 million monthly active users, using Python, PyTorch, and cloud-native serving stacks.
ML Engineer at Toss builds and deploys recommendation, search, and AI models across fintech and ecommerce to optimize product exposure, CTR, and user experience using PyTorch, Spark, and Kubernetes.
Designs and operates ultra-high-performance AI infrastructure (GPU clusters, InfiniBand, Kubernetes) for Toss Securities' ML services, optimizing resource use and eliminating bottlenecks.
Build, fine-tune, and operate LLM/NLP models to simplify complex financial-securities information into user-friendly services at a Korean fintech company.
Build and operate ML/LLM platforms for a Korean neobank, focusing on stable, scalable, and secure model training, deployment, and serving using tools like MLflow, Kubeflow, Triton, and vLLM.
Builds and operates a machine-learning platform for a securities app, focusing on LLM serving, gateway systems, and MLOps tooling in Kubernetes.
Builds and deploys AI/ML systems for banking services like fraud detection, lending models, and LLM-based agents in a high-traffic, regulated environment.
Designs, builds, and operates scalable data pipelines for AI/ML products in banking, ensuring reliable batch processing, feature engineering, and model inference workflows using distributed systems like Spark and Airflow.
Build ML models for credit risk, loan needs, and card approvals within Toss’s financial marketplace, using Python/SQL and deep learning to tailor user services.
Building data pipelines, maintaining ML models, and integrating diverse data sources into a unified analytics environment using Databricks, MLflow, and PySpark for GenAI and agent-based solutions.
Build and operationalize ML solutions using Azure and Databricks, translating business requirements into scalable, maintainable ML workflows within a Scrum team of data engineers and analysts.
Junior ML Engineer supporting the Data & AI team at a global tea company, building computer vision, forecasting, classification, and LLM workflows using Python, Databricks, and Azure.
ML Engineer building scalable data pipelines and GenAI/Agentic AI solutions on Databricks Lakehouse using PySpark, MLflow, and Python AI frameworks for consulting clients across Europe.
Senior ML Engineer leading end-to-end ML pipelines at Lipton Teas & Infusions, using Databricks and AzureML to build scalable ML services across computer vision, forecasting, and LLM-enabled workflows while mentoring the ML chapter.
Responsibilities Designs, develops, tests, and operationalizes algorithms to solve complex business challenges and mission requirements. Models are built to learn from data and generate predictions aligned with…
Overview Working at Atlassian Atlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals,…
Senior ML Engineer building reusable AI models and infrastructure (Rust/Python simulation engine, grid-data toolkits, ML libraries for forecasting/disaggregation) for utility and energy clients at a climate-focused AI startup.
White Collar Factory (95009), United Kingdom, London, London Staff Software Engineer - Machine Learning About this role We’re on a mission to transform the way we use data and AI to service our customers and drive…
Build and deploy ML models to predict printability of complex CAD parts using geometric, image, thermal, and simulation data, while designing data collection processes at a metal 3D printing company.
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