951 to 975 of 1,002 Data Pipeline Jobs in the UK

energy technology platform to hire their first Head of Applied AI Research. \n \n The energy grid is one of the most complex, data-rich, highest-stakes systems on the planet. Our client has built the operating system for it. \n \n You've likely heard of them … through their flagship client, who powers 40% of the UK energy market. They process 15 billion data points daily across 70 million households worldwide, hold $500m in contracted ARR, and are expanding rapidly across Europe, Australia, Japan and the US. \n \n They are, by any measure ...

making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA … distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency ...

making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA … distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency ...

making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA … distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency ...

making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA … distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency ...

making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA … distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency ...

making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA … distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency ...

making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA … distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency ...

making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA … distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency ...

making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA … distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency ...

making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA … distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency ...

making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA … distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency ...

making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA … distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency ...

making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA … distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency ...

making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA … distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency ...

/Xilinx RFSoC (Zynq UltraScale+) and Intel FPGA platforms (Agilex/Stratix) Integrate with high-speed ADC/DAC interfaces and real-time data pipelines Work across VHDL, SystemVerilog and MATLAB/Simulink HDL workflows Support lab-based integration, test and evaluation using RF instrumentation Contribute to architectural … analysers) Experience leading or mentoring engineers Desirable experience Embedded Linux or bare-metal C development High-speed interfaces (AXI, JESD204, memory architectures) Streaming data architectures and high-throughput signal handling Experience within EW, radar or spectrum-related systems Our non-negotiables Due to the highly secure nature ...

metadata enrichment at scale Collaborate with product, policy, and engineering teams to translate content intelligence into user impact Contribute to evaluation frameworks, data pipelines, and annotation systems Support rapid experimentation to prototype and launch new types of content signals Help improve system reliability, scalability, and performance across large … comfortable working with ML frameworks such as PyTorch, TensorFlow, or similar You have experience working with large datasets and care about data quality and evaluation You are interested in or have worked with multimodal machine learning You understand how to design systems that balance automation with quality ...

models are necessary, they are not sufficient. The hard problem is systems intelligence: end-to-end architectures that understand a company's code, data, and decisions, and improve themselves through experience. We are assembling a small, senior team of researchers and engineers obsessed with systems-first intelligence. … survives in production, not just notebooks. The Full ML Lifecycle: Expertise in end-to‐end system design: from feature stores and real‐time data pipelines (Kafka/Beam) to A/B testing infrastructure and model monitoring. You understand the nuances of online vs. offline evaluation and have ...

environment to bridge financial business needs with technical systems, transforming how engineering teams capture, manage, and scale core repo trade and positions inventory data pipelines. Your Impact: Platform Innovation: Analyze and translate complex financial business requirements into scalable technology system changes for repo trade capture and positions inventory … least one of three core streams: Repo eTrading (RFQ, STP, ION/Bloomberg platforms), Collateral Optimization (clearing, margins), or Term Repo Lifecycle Management. Data & Assets: Strong basic understanding of Fixed Income assets (Bonds, US Treasuries) alongside moderate relational database and SQL skills. Domain Tools: Complete mastery ...

critical solutions for National Security customers - building products that protect people, platforms, missions and information against evolving threats. You’ll help customers use data to improve mission outcomes, lead teams hands‐on, and mentor others while working at the forefront of innovation. What you’ll be doing … patterns across established and emerging technologies. Convert designs into working implementations and document to required standards. Develop and maintain APIs, UI components and data pipelines within existing data infrastructure. Lead and contribute to agile ceremonies (stand‐ups, refinement, planning, demos). Keep the team aligned ...

National Security customers - building products that protect people, platforms, missions and information against evolving threats. \n \n You’ll help customers use data to improve mission outcomes, lead teams hands-on, and mentor others while working at the forefront of innovation. \n \n What you’ll be doing … established and emerging technologies. \n Convert designs into working implementations and document to required standards. \n Develop and maintain APIs, UI components and data pipelines within existing data infrastructure. \n Lead and contribute to agile ceremonies (stand‐ups, refinement, planning, demos). \n Keep the team ...

support and assist the manufacturing process. The successful candidate will join the development team responsible for building and integrating real-time test and data capture systems. The role will focus on developing and maintaining software in a Linux environment, working closely with high-speed multi-channel SFPDP cards … real-time data flows, and complex multi-threaded processing pipelines. Proficient in C++ and with knowledge of C#, you will play a critical part in the design, development, integration, and verification of test software solutions that are central to our system validation and delivery capabilities. Responsibilities: Design ...

work within a lean, agile team to bridge frontend development with backend systems, transforming how internal engineers interact with, validate, and secure core data pipelines. Your Impact Platform Innovation: Build and scale developer-facing tools and dashboards from scratch to manage data contracts at the code … level. Infrastructure Safeguards: Protect downstream systems (like recommendation engines) by implementing code-level guardrails against data incidents. Capacity Expansion: Act as a critical accelerator for a tight-knit core team, driving rapid prototyping during an essential discovery MVP phase. Cross-Functional Tooling: Balance frontend utility with data ...

that takes the science seriously. The problem space You’ll be building vision models that need to work in the real world – noisy data, scarce labels, wildly variable conditions, and strict compute constraints. The core challenges are around data efficiency, cross-domain generalisation, and running accurate … beyond Grounding in classical signal processing and CV fundamentals Experience owning projects end to end, not just modelling in isolation Comfortable building custom data pipelines from raw, messy, real-world data Edge inference or efficient model deployment experience is a genuine plus The role London office ...

Title: Engineering Manager Data Platforms SENIOR LEVEL Hybrid - Sheffield 2/3 days per week Inside IR35 Contract Job Description This role leads a high-performing data engineering team focused on building and operating modern data platforms using cloud-native technologies. You will oversee … design, development and optimisation of large-scale data pipelines, ensuring robust, secure and efficie... LFWQ1_UKTJ ...