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The Staff Data Scientist will build and refine credit underwriting models for NIRA's consumer credit platform in India. This role involves using Python, SQL, and machine learning to predict loss performance and leverage alternative data to expand financial access.
The Data Scientist will develop and deploy AI agents and LLM-based solutions to automate business processes within Sberbank. The role involves fine-tuning LLMs, implementing RAG, and solving complex NLP tasks using Python and modern AI frameworks.
The AI Engineer will optimize and deploy machine learning models, including LLMs and vision models, across heterogeneous hardware platforms like CPUs, GPUs, and NPUs. The role focuses on improving inference performance through techniques such as quantization, model compression, and hardware-specific acceleration.
Junior Data Scientist builds and improves machine learning pipelines for recommendation systems using Python, PySpark, and PyTorch to personalize user experiences across banking, e-commerce, and other sectors.
Data Scientist building an in-house coupled simulation framework for dense plasma focus fusion machines, including radiation models, surrogate models, and data pipelines for shot diagnostics—primarily using Python (NumPy, SciPy, JAX/PyTorch) with C++/Fortran as needed.
Build and optimize large-scale deep learning models and data pipelines, focusing on sequence and time-series architectures, using Python, PyTorch/TensorFlow, and MLOps practices.
Develops and deploys AI/ML models (classification, NLP, CV, time-series) for business use, using Python, TensorFlow/PyTorch, and cloud platforms like AWS SageMaker. Focuses on MLOps (Docker, MLflow, Kubernetes) and cross-team collaboration to solve real-world problems with automation and predictive analytics.
Junior ML engineer builds and tunes AI models for tax document automation, using Python, PyTorch/TensorFlow, and Azure AI tools in Irvine, CA.
Forward Deployed Engineers at Baseten work directly with major AI companies, owning technical outcomes for AI models including inference and post-training systems. They act as technical leads, debug production issues, and contribute to product development across multiple client accounts.
Manages cyber cloud security and AI initiatives, including project delivery, strategic leadership, risk management, and team leadership. Core technologies include AI tools (Python, R, TensorFlow, PyTorch) and regulatory frameworks (NIST, ISO).
Join RBC Borealis as a Machine Learning Researcher, developing novel AI solutions (generative AI, NLP, time series analysis) using Python and deep learning tools, with access to massive datasets and computational resources.
Designs and builds scalable AWS-based ML/data infrastructure for enterprise AI solutions, focusing on MLOps, feature engineering, and governance to enable production-ready AI at scale.
Ship production AI features and design autonomous agents, multi-agent workflows, and ML pipelines on the Trend Vision One platform using Python, GenAI, and PyTorch or TensorFlow.
Build AI inference systems that serve large-scale models with extreme efficiency, optimizing GPU kernels and compilers, working with vLLM, CUDA, and other AI/ML technologies to push the frontier of accelerated computing.
Research and develop AI-driven autonomy for drones, integrating learning-based methods with classical robotics to deploy intelligent flight systems in real-world environments.
As a Machine Learning Research Engineer at RBC Borealis, you’ll build ML-based software solutions, collaborate with stakeholders, prototype algorithms, and integrate them into products using Python 3.x, PyTorch, JAX, or Tensorflow, focusing on generative AI, NLP, and time series analysis.
Design and implement AI/ML features for cybersecurity solutions, focusing on autonomous systems, multi-agent workflows, and production pipeline management for the Trend Vision One platform.
The Algorithm Expert will design and implement foundation models for financial behavior sequences to power risk-control systems for DiDi's global fintech business. The role involves building pre-training pipelines, multi-source sequence modeling, and deploying models for fraud detection and credit scoring.
Designs and optimizes large-scale LLM serving infrastructure at LinkedIn, focusing on GPU performance, cost efficiency, and latency improvements for AI models in production.
Machine Learning Engineer at Deeter Analytics building deep-learning models on market data, working end-to-end from raw data to production models using Python, PyTorch, and GPU infrastructure in a fully remote role.
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