Software Engineer, ML Infrastructure

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 third‑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 BS or MS in computer science with focus in data engineering and/or machine learning. 3+ years of industry experience building, running and improving large-volume data processing, feature extraction, data annotation workflows. Experience building data mining and search capabilities. Experience with both Python and SQL is required. Solid understanding of data distributions and their impact on ML models. Hands‑on experience and good understanding of LLMs, VLMs, embeddings, vector databases. Experience with data annotation providers such as CVAT, LabelBox, LabelStudio, etc. What Makes You Stand Out Experience with integrating cloud inference platforms for LLMs/VLMs (ChatGPT, Gemini, etc). Experience working with multi‑modal data (LiDAR, camera, etc). Experience with robotics systems. Experience optimizing large‑scale vector databases. #J-18808-Ljbffr