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Our data platform team is working on accelerating autonomous driving by providing access to petabytes of data collected by our fleet of autonomous and non-autonomous vehicles. Efficient, fast and cost-effective access to data at a large scale is key to tackle the hardest problems in AD/ADAS, from developing the Machine Learning (ML) models for perception and prediction of human driving patterns, to increasing the sophistication of our validation and simulation by identifying rare and interesting real-world driving situations. The data ecosystem developed by the London team is a key building block for developing and testing modern AD/ADAS products that will impact millions of customers.
Our ML and Data pipelines are built on top of the open-source Flyte orchestration framework and are deployed to AWS. Pipeline code is written in Python. We use SQS and Kafka to automate data connections and leverage BigQuery and ElasticSearch for data storage. We believe strongly in automation and testing to ensure the delivery of robust and correct systems. We are a distributed team, working in the UK and US.
Who are we looking for?
The London Data Infrastructure team is looking for engineers who are passionate about and enable the next generation of automotive software development. The right candidate will have excellent communication skills, solid coding skills, broad knowledge of software development across areas such as Data Infrastructure and Warehouses, Data Ingestion, Compute Frameworks, Observability, and Build Infra.
Responsibilities:
Work on high-impact projects and innovate new solutions to problems in the self-driving space.
Work with Computer Vision and Machine Learning engineers on high-impact projects and innovate new solutions to problems in the self-driving space.
Understand the complex data requirements of modern ML development and tailor our data ecosystem to these needs.
Build efficient data pipelines for ingestion from the vehicle fleet.
Work on distributed systems that serve, process and transform large quantities of data in the cloud.
Minimum Qualifications:
Extensive experience in Python (or other object-oriented language).
Experience building reliable, distributed applications for Data Processing or similar areas.
Working with RPC protocols such as gRPC/protobuf.
Hands-on experience developing cloud applications (e.g. AWS, GCP, Azure).
Preferred Qualifications:
Experience writing testable and modular code.
Experience working in a fast-paced environment, collaborating across teams and disciplines.
Experience designing, deploying, and maintaining distributed systems.
Data pipelines, data platforms, workflow orchestration, batch processing.
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