Software Engineer, ML Infrastructure

Software Engineer

At Serve Robotics, we're reimagining how things move in cities. Our personable sidewalk robot is our vision for the future. It's designed to take deliveries away from congested streets, make deliveries available to more people, and benefit local businesses.

The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles, Miami, Dallas, Atlanta and Chicago while doing commercial deliveries. We're looking for talented individuals who will grow robotic deliveries from surprising novelty to efficient ubiquity.

Who We Are

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.

As a Software Engineer on the Machine Learning (ML) Infrastructure team, you will help design, build, and maintain our petabyte-scale data and ML platform that powers data partnerships, ML research, and autonomy engineering. You will build and improve our data discovery capabilities and integrate with 3rd party annotation platforms. By collaborating with members of the autonomy and ml teams you will help us refine how we organize various data attributes and classifications. This role plays a pivotal role in helping the team leverage data from our rapidly expanding fleet of thousands of robots.

Responsibilities

• Develop and maintain highly scalable data processing pipelines for data curation, annotation, search and ml feature extraction.
• Build data discovery features for the platform.
• Create and maintain search features such as natural language querying
• Develop and maintain our orchestration and scheduling systems.
• Maintain and evolve our data schemas such as unified data attribute system, scenario tagging and management
• Build integrations with annotation providers to efficiently review large scale data preannotations
• Collaborate with autonomy engineers to collect feedback, improve documentation, and run tutorials on platform features

Qualifications

What Makes You Stand Out