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# Data Engineer (Autonomous Systems)

- **Company:** Ellison Institute, LLC
- **Location:** Oxford, UK
- **Salary:** £65,342.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Storage, Software Quality, Computer Programming, Databases, Data Infrastructure, Data Systems, Graph Database, Python (Programming Language), Machine Learning, Message Queuing Telemetry Transport (MQTT), Rapid Prototyping Process, Raw Data, Robotic Automation Software, Data Ingestion, Fastapi, Data Layers, Apache Flink, Apache Kafka, Graphql, Machine Learning Operations, Stream Processing, Software Version Control, Data Pipelines
- **Published:** October 4, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5909784759

## About the Role

* You have strong programming experience in Python, and value code quality, reliability, and performance while adapting to an evolving industry.
 * You think in systems and own them end-to-end - from device output to APIs - and embrace long-term engineering rather than one-off scripts.
 * Experience with real-time data streaming - Kafka or Pub/Sub (or equivalent), and the challenges that come with high-frequency, low-latency data capture.
 * You treat reproducibility, version control, schema design and lineage as the things that make good science possible.
 * A clear, respectful communicator who can collaborate with scientists, roboticists and researchers.

## Description

At EIT we are seeking an experienced Data Engineer to join our Scientific Compute and Data team. Our Data Engineers in Autonomous Systems work within a wider project, AutoLabs. This is where EIT automates the scientific discovery loop: experimental design, execution, analysis, and the learning that feeds back into the next experiment and the lab itself. The AutoLabs data platform captures, stores and serves high-frequency multimodal data. It spans an edge-to-cloud telemetry stack (MQTT, Kafka, media streams such as HLS), streaming APIs, and a growing set of hardware integrations across multiple sites including robotic systems, sensor rigs and lab instruments.

Our labs produce raw data for a fast-growing set of models: vision-language-action models, orchestration and monitoring models, and world models that learn the dynamics of a laboratory to simulate experiments before they are run.

As a Data Engineer, you'll work directly alongside AI researchers, software engineers, robotics engineers, and domain scientists to build the data layer that makes our autonomous operations possible. You will contribute to an engineering culture that values robust system design, testing, and deep collaboration, but allows flexibility for rapid prototyping and responsiveness to changing landscapes.

Day-to-Day, You Might:

 * Build high-throughput, low-latency ingestion for live video, sensor telemetry, robot joint state and instrument control signals, integrating validation at the edge before data reaches the cloud.
 * Work across the edge-to-cloud stack, from edge devices and MQTT brokers through to Kafka topics, cloud storage, and the APIs that serve scientists and ML pipelines.
 * Turn streams of robot state, instrument output and video into structured database transactions: which arm did what to which sample, when, and what came out.
 * Build the knowledge graph on top of that, linking inputs to results, so that a fact can always be traced back to the video segment and sensor readings behind it.
 * Make the output model-ready: the formats, sampling, labelling and provenance that training and evaluation need, working directly with the researchers who consume it., * Automated lab or life sciences environments - instrument APIs, lab protocols, and what scientists expect of data quality.
 * Data pipelines for physical systems: robotics (e.g. MQTT), autonomous vehicles, lab or clinical instruments, industrial control, or similar environments.
 * Knowledge, entity, search or feed systems where correctness under continuous update was the hard part.
 * Stateful stream processing (Flink, Kafka Streams, Beam or similar): event time, watermarks, exactly-once.
 * APIs for data ingestion and consumption with FastAPI, gRPC or GraphQL.
 * Graph stores and entity resolution or curation at scale, where the interesting decisions are about what to keep, merge or drop.
 * Experience working close to model development, such as curating training or evaluation data, or building the data systems that feed models. You don't need to be a machine learning engineer, but you should know what makes a dataset good.