Uppercopy
Mini tools
Senior Data Engineer, ML Infrastructure
Serve Robotics
Remote
Full-time
$185,000 -
$235,000
About Serve Robotics
We are tech industry veterans in software, hardware, and design who are pooling our skills to build the future we want to live in. We are solving real-world problems leveraging robotics, machine learning and computer vision, among other disciplines, with a mindful eye towards the end-to-end user experience. Our team is agile, diverse, and driven. We believe that the best way to solve complicated dynamic problems is collaboratively and respectfully.
About the Role
As a Senior Data Engineer in the Machine Learning (ML) Infrastructure team you will be helping us build out our petabyte scale data platform supporting data partnerships, ML and autonomy engineers. Your work will directly impact a new revenue stream through commercialization of our robot data. You will be focusing on building highly scalable data pipelines and improving data discoverability features. You will collaborate with ML engineers in the creation of diverse large scale datasets used to train cutting edge ML models that are deployed to our fleet of thousands of robots.
Qualifications
5+ years of professional experience in software or data engineering.
Strong programming proficiency in Python, SQL.
Hands-on experience building and maintaining large-scale data processing pipelines using cloud technologies.
Proficiency with data warehousing and ETL/ELT concepts.
Solid understanding of system design, along with data privacy and security best practices.
Strong programming proficiency in Python, SQL.
Hands-on experience building and maintaining large-scale data processing pipelines using cloud technologies.
Proficiency with data warehousing and ETL/ELT concepts.
Solid understanding of system design, along with data privacy and security best practices.
Responsibilities
Architect and implement robust, scalable data pipelines to process, synchronize, and package robotics data (e.g., LiDAR, camera, IMU, proprietary maps) for third-party consumption.
Build a data processing and egress platform, ensuring the timely and accurate delivery of datasets according to strict partner SLAs.
Create data lifecycle policies to control cloud data costs. Build and maintain a universal data catalogue of all raw robot data. Create cost monitoring, attribution and alerting systems.
Build data discoverability platform features, use ml models to generate new attributes and maintain efficient, highly scalable search indexes.
Setup data access audit trails and strong security controls managed through IaC. Create lineage maps and expose data traceability capabilities to internal consumers.
Build a data processing and egress platform, ensuring the timely and accurate delivery of datasets according to strict partner SLAs.
Create data lifecycle policies to control cloud data costs. Build and maintain a universal data catalogue of all raw robot data. Create cost monitoring, attribution and alerting systems.
Build data discoverability platform features, use ml models to generate new attributes and maintain efficient, highly scalable search indexes.
Setup data access audit trails and strong security controls managed through IaC. Create lineage maps and expose data traceability capabilities to internal consumers.
