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Career Category Clinical Job Description HOW MIGHT YOU DEFY IMAGINATION? If you feel like you’re part of something bigger, it’s because you are. At Amgen, our shared mission -to serve patients - drives all that we do.…
Mapbox is the leading real-time location platform for a new generation of location-aware businesses. Mapbox is the only platform that equips organizations with the full set of tools to power the navigation of people,…
Cloud Data Infrastructure Engineer (m/f/d) Senckenberg Society for Nature Research (SGN) , headquartered in Frankfurt am Main, is seeking to fill the following position in the Department of the Senckenberg Data and…
A DevOps engineer will support a research organization's earth observation systems, maintaining cloud platforms and big data infrastructure, resolving infrastructure-as-code tickets, and improving monitoring and security. Core stack includes AWS/Azure/GCP, Kubernetes, Helm, Terraform, ArgoCD, Jenkins, and Linux.
A senior DevOps engineer maintaining cloud infrastructure for large-scale image processing and data analytics workloads. Day to day involves resolving infrastructure tickets with IaC, deploying components to public cloud, running Kubernetes and Linux environments, monitoring/logging, and applying security best practices.
Senior Data Engineer who designs, builds, and owns scalable data and AI solutions end to end — from data pipelines and BI to Generative/Agentic AI with RAG, MLOps, and LLMOps in production — using Python, SQL, Spark, Databricks, LangChain, Airflow, and Terraform across AWS, Azure, and GCP.
Senior Data Scientist builds ML models (XGBoost, time-series, recommendations) end-to-end, engineers features from terabyte-scale data, and deploys solutions in production for a top-3 Russian digital bank.
Чем предстоит заниматься Проектирование и разработка ETL/ELT-пайплайнов: сбор, обработка и трансформация больших объёмов данных из различных источников (API, баз данных, логов, внешних систем) в хранилище. Разработка и…
The Data Scientist / Developer applies their academic training, analytical capability and interest in the organisation and interpretation of data to the development of Blunomy’s data and digital solutions. The role…
A Python developer joins a data engineering team to design, build, and maintain high-performance data processing pipelines for large-scale datasets. Day-to-day work centers on pandas/polars, ClickHouse, and NATS, with Docker/Kubernetes deployments, Dask for distributed computing, and pytest-tested code managed in Git.
Build and operate ML pipelines for SQC's quantum-enhanced AI system, Watermelon. You will manage data ingestion, training/evaluation workflows, and experiment tracking while benchmarking quantum features against classical baselines using Python, PyTorch/JAX, and orchestration tools like Airflow or Dagster.
Across Commodities and Global Markets, technology sits close to the decisions that matter. Financial markets move around the clock, and the technology that supports them needs to move with them. Our quantitative teams…
Unique opportunity to join this hugely successful prop trading firm as they scale significantly, building out a brand-new central trading team. With major investment in GPUs and compute infrastructure, the aim is to…
Exciting opportunity at one of the fastest growing financial services firms around the world. They offer prime brokerage, clearing and financing across traditional and digital assets, and are now looking to hire…
Contract Lead Python Developer working with PT. Kredit Utama Fintech Indonesia: designs Python backend services and ML/data pipelines (XGBoost, Dask/Spark, Kafka), performs code reviews and CI/CD work, and maintains OJK-compliant audit trails. Full-time on-site in Jakarta Pusat or BSD Tangerang.
A Toronto-based data engineering role building and optimizing high-performance data pipelines in Python. Day to day the developer works with pandas/polars for large dataset manipulation, Docker and Kubernetes for deployments, ClickHouse for analytics, NATS for event-driven messaging, and Dask for distributed computing, with pytest-based testing.
A Python developer on a data engineering team in Toronto (hybrid, 4 days in office) who designs, builds, and maintains high-performance data pipelines for large datasets. Core tech includes pandas, polars, Docker, Kubernetes, ClickHouse, NATS, Dask, pytest, and Git.
Data Engineer building the core data platform for a leading global hedge fund's Multi-Asset Arbitrage team in Singapore: petabyte-scale ingestion, ETL, storage and batch/streaming pipelines supporting quant research, backtesting and portfolio management. Core stack: Python, SQL, Pandas/Polars/Dask/PySpark, Kafka, Redis, Parquet/Arrow. Singapore citizens/PR only.
An AI Engineer who designs, develops, and deploys machine learning models for production — building training/evaluation pipelines, monitoring model performance, and integrating AI into applications alongside data scientists and product teams. Core stack is Python with TensorFlow/PyTorch/scikit-learn, cloud platforms, and ML Ops tooling.
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