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# Lead Machine Learning Engineer

- **Company:** Serve Robotics
- **Location:** Los Angeles, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $177,000.0 - $215,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Neural Networks, Computer Vision, Computer Clusters, Computer Programming, Extract Transform Load (ETL), Distributed Computing Environment, Memory Management, Fault Tolerance, Design of User Interfaces, Hardware Design, Human-Computer Interaction, Python (Programming Language), Machine Learning, Graphics Processing Unit (GPU), Delivery Pipeline, Model Validation, Usage Tracking, Loss Functions, Information Technology, Optimization Algorithms, Data Management, Machine Learning Operations, Lidar, Data Pipelines
- **Published:** October 29, 2026
- **Apply:** https://www.careerbuilder.com/job-details/lead-machine-learning-engineer-los-angeles-ca--6c8778ba-bb59-40d1-97e7-8687a61fb2e7

## About the Role

Machine Learning, Vehicle Fleets, Ubiquity, Computer Vision, Robotics, Machine Learning, User Interface/Experience (UI/UX), Hardware Design, Problem Solving Skills, Data Management, * 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.

What Makes You Stand out

 * Experience identifying and resolving training bottlenecks related to compute utilization, memory usage, and data throughput in machine learning systems.
 * Experience training machine learning models on robotics or autonomous driving datasets involving multimodal sensor inputs such as camera video, LiDAR point clouds, radar, or telemetry data.
 * Experience developing models that combine multiple data modalities (e.g., images, point clouds, and structured sensor data) into a unified learning system.
 * Peer-reviewed publications or significant research contributions in machine learning, robotics, or related areas.

## Description

This role develops and scales large-scale machine learning training systems for multimodal robotics data, enabling the creation of high-performance autonomy models. By optimizing distributed training pipelines, neural network architectures, and data processing workflows, the position improves training efficiency, accelerates model iteration, and maximizes GPU utilization. The role collaborates closely with ML researchers and infrastructure teams, influencing the design, deployment, and performance of end-to-end autonomy models and the large-scale data pipelines that support them., * 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.