8 of 8 Distributed Computing Jobs in London

Experience writing complex joins, stored procedures, and performing performance tuning. Big Data Concepts Foundational understanding of Big Data architecture (Data Lakes, Data Warehouses) and distributed processing concepts (e.g., MapReduce). ETL/ELT Basic knowledge of ETL principles and data modeling (star schema, snowflake schema). Version Control Practical … experience with Git (branching, merging, pull requests). Preferred Qualifications (A Plus) Experience with a distributed computing framework like Apache Spark (using PySpark). Familiarity with cloud data services ( AWS S3/Redshift, Azure Data Lake/Synapse, or Google BigQuery/Cloud Storage ). Exposure to workflow ...

significant amounts of information with attention to detail and accuracy. Adept at queries, report writing, and presenting findings. Experience working with large datasets and distributed computing tools (Hadoop, Spark, etc.) Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, etc.). Experience with data ...

like Hadoop or Spark. • Familiarity with data visualization tools such as Tableau or PowerBI. • Understanding of reinforcement learning. • Knowledge of neural networks. • Experience with distributed computing. • Familiarity with software development methodologies like Agile or Scrum. ...

Vault Strong programming skills in Python and SQL. Experience building scalable, production-grade data pipelines. Understanding of data modelling, data warehousing concepts, and distributed computing. Familiarity with CI/CD, version control, and DevOps practices. Nice-to-Have Experience with streaming technologies (e.g., Spark Structured Streaming, Event Hub, Kafka ...

Spring MVC, and related technologies. Database Technologies: Strong SQL skills; experience with relational databases (e.g., Oracle, SQL Server, PostgreSQL) and/or NoSQL solutions. Distributed Systems: Understanding of distributed computing concepts and technologies (e.g., microservices, caching, clustering). Testing Frameworks: Experience with JUnit, Mockito, or similar testing ...

Responsibilities: - Create robust, flexible and scalable ML tooling and infrastructure which supports research scientists to leverage Client’s powerful infrastructure (through e.g. source control, distributed compute clusters, data storage)- Work collaboratively as part of a multifunctional team where communication, documentation and teamwork are highly valued- Write clean, maintainable code … learning, including capture and annotation.- Demonstrated experience implementing and evaluating working and end-to-end prototypical learning systems.- Experience working with high performance or distributed compute solutions.- Deployment and continuous integration experience. Preferred Qualifications: - Familiarity with Machine Learning for Audio, multimodal or DSP purposes- Experience writing scalable ML tooling ...

learning research actually work at scale - this one's for you. You'll develop and improve the internal tooling that researchers rely on daily: distributed compute workflows, data pipelines, and robust infrastructure that connects research code to real environments - including hardware/prototype devices on-site. What … maintain scalable Python tooling that supports ML research workflows Improve reliability: fix bugs, optimise performance, and ship iterative enhancements in sprints Integrate with distributed compute, data storage, and internal development systems Work closely with researchers as your "users" - gather requirements, translate ambiguity into working software Write clean, maintainable code ...

cutting‐edge model code into scalable, reliable systems used in real‐world applications. This is a high‐ownership role suited for someone who loves distributed systems, multi‐GPU scaling, model optimization, and fast iteration. What You'll Do Build and optimize training & inference pipelines for large models (Transformers, SSMs … Diffusion, etc.) Scale workloads across multi‐GPU and distributed systems Optimize model performance, latency, memory usage, and throughput Productionize research code into robust, repeatable systems Work closely with researchers to convert exploratory notebooks into production frameworks Own ML infrastructure components — data loading, distributed compute, experiment tracking Design modular ...