Senior Data Scientist - Machine Learning

January 17

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Serve Robotics

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Serve Robotics is an innovative company focused on revolutionizing the delivery industry with its autonomous delivery robots. The company aims to make delivery services more affordable, sustainable, and convenient by using self-driving robots instead of traditional two-ton vehicles for small deliveries like burritos. Through a commercial deal with Uber, Serve Robotics plans to deploy up to 2,000 robots, marking a significant advancement in the autonomous delivery sector.

51 - 200 employees

Founded 2017

🚗 Transport

🤖 Artificial Intelligence

💰 $30M Venture Round on 2023-08

📋 Description

• 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 while doing commercial deliveries. • We’re looking for talented individuals who will grow robotic deliveries from surprising novelty to efficient ubiquity. • Serve Robotics aims to develop dependable and proficient sidewalk autonomy software. • We are looking for a talented Senior Data Scientist who bridges the gap between ML infrastructure and ML engineers. • The ideal candidate possesses strong fundamentals in machine learning, with the ability to prototype and train learning-based models using data-centric techniques. • This individual should have expertise in data ETL processes, SQL queries, and building scalable data pipelines to make data accessible for model training.

🎯 Requirements

• Master’s in Computer Science, Data Science, or a related technical field and 5+ years of industry experience in data engineering, machine learning, or a similar domain. • Strong proficiency in Python and SQL, with demonstrated experience building data pipelines at scale and ETL workflows that cater to multi-modal data (e.g., images, point clouds, time-series data). • Proven ability to work with PB’s of datasets, including structured, semi-structured, and unstructured data. • Hands-on experience working with ML frameworks such as TensorFlow, PyTorch, or similar. • Solid understanding of ML fundamentals and data-centric techniques for model training. • Experience with cloud platforms (GCP, AWS, or Azure) and tools like Kubernetes, Docker, and Airflow. • Excellent communication skills and the ability to collaborate with cross-functional teams.