Lead Machine Learning Engineer

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🕒 March 28

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Serve Robotics

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2017

📦 Logistics

🍽️ Food & Beverage

🚗 Transport

💰 $30M Venture Round on 2023-08

Logistics • Food & Beverage • Transport

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.

📋 Description

• Design and maintain training systems that can process and learn from petabyte-scale multimodal datasets (e.g., video and point cloud data). This includes ensuring data is efficiently loaded, distributed, and processed across large GPU clusters. • Identify and resolve bottlenecks in the training pipeline, including data loading, preprocessing, model computation, and inter-node communication, to maximize GPU utilization and reduce training time. • Work with the ML team to develop and refine neural network architectures suitable for autonomy tasks, particularly those handling high-dimensional and sequential sensor data. • Create and adjust loss functions and training strategies that help the model learn effectively from complex multimodal inputs and improve autonomy performance. • Configure, monitor, and maintain large-scale distributed training jobs across multiple machines and GPUs, ensuring stability, fault tolerance, and efficient resource usage. • Implement scalable systems to preprocess, transform, and augment large robotics datasets so that they are suitable for model training. • Work closely with ML scientists and other engineers to integrate new models, experiments, and training approaches into the production training pipeline. • Analyze training metrics, model outputs, and experiment logs to assess model performance and guide improvements in architecture, data usage, or training strategies. • Develop tools and workflows that allow teams to run experiments, track results, and iterate quickly on new model ideas or training approaches.

🎯 Requirements

• Master’s or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a closely related technical discipline. • Minimum of 5 years of professional experience developing, training, and deploying machine learning models in production environments. • Hands-on experience training machine learning models across multiple GPUs or compute nodes, including familiarity with distributed training frameworks and large dataset handling. • Strong programming skills in Python for implementing machine learning models, data pipelines, and training workflows. • Solid knowledge of core concepts such as neural networks, optimization algorithms, loss functions, model evaluation, and training methodologies.

🏖️ Benefits

• Offers Equity