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Builds and deploys end-to-end AI/ML models (forecasting, classification, LLMs) for clients, owning data, methodology, and production pipelines while mentoring junior scientists.
The Lead Data Scientist will architect and develop AI models for digital dentistry, focusing on 3D computer vision, generative AI, and anatomical reconstruction. This hands-on leadership role involves transitioning R&D into production-ready systems and defining the technical roadmap for the company's AI platform.
The Senior Data Scientist will design, develop, and deploy AI models using transformer architectures and foundation models to analyze telemetry data for ArcGIS users. The role involves fine-tuning models with LoRA/QLoRA, building evaluation frameworks, and collaborating with cross-functional teams to deliver production-ready AI solutions.
Co-op ML software engineer building end-to-end ML solutions—from data preprocessing to front-end—at RBC Borealis, using Python/C++/Java, Spark/Hadoop, and deep learning frameworks.
Builds AI-driven automation frameworks and agentic workflows to solve business challenges, integrating solutions into SIXT’s tech ecosystem. Designs scalable AI systems, collaborates with stakeholders, and ensures global performance.
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9…
The Data Science Architect designs and leads the delivery of AI and data solutions for clients using GCP services and various machine learning frameworks. The role involves technical leadership, mentoring junior staff, and bridging the gap between complex data challenges and business outcomes.
Staff ML Engineer (Ops/Release) responsible for owning release gates across complex model training phases, ensuring quality/safety standards, and driving improvements in ML delivery pipelines using PyTorch, TensorRT, GitHub Actions, and MLOps tooling.
АО «ВИМ Инвестиции» — признанный лидер на российском рынке управления инвестиционными фондами. Мы создаем передовые финансовые продукты, помогая крупным частным клиентам и корпорациям эффективно управлять капиталом и…
Solution Engineer at Snowflake partners with customers to demonstrate and implement the AI Data Cloud platform, conducting demos, POCs, and technical evaluations to drive adoption and value.
Optimize and deploy ML models for embedded automotive autonomy, balancing model quality with hardware efficiency using quantization, mixed-precision, and hardware-aware techniques.
The Machine Learning Engineer will develop generative world models and interactive simulation environments to advance autonomous driving research. The role focuses on creating efficient, real-time models using Python and PyTorch to improve training and evaluation for self-driving systems.
This role involves building and scaling generative world models to produce synthetic data for autonomous driving research. You will bridge the gap between ML research and engineering by optimizing model architectures and integrating synthetic data into the training pipeline for driving models.
Design and deploy ML models directly onto custom hardware, co-architecting solutions with traders and engineers while optimizing for latency and resource constraints.
Build and optimize large-scale ML training and inference pipelines for low-latency trading systems using Python, CUDA, PyTorch/TensorFlow, and GPU acceleration.
Research and deploy ML models to generate trading signals and optimize portfolios, collaborating with quants and engineers using Python and PyTorch/TensorFlow.
Researcher builds deep-learning models to predict short-term market moves for high-frequency delta-one trading, using TensorFlow/PyTorch and collaborating with traders and engineers.
Research and implement ML models for global trading strategies, deploying solutions in equities, futures, and options markets using Python and frameworks like PyTorch.
Research intern designing ML models to analyze market data and inform trading strategies in equities, futures, and options.
Research and implement ML models for global trading strategies in equities, futures, and options markets using Python and PyTorch.
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