Lead Machine Learning Engineer
Serve Robotics5 months ago
Base Salary
$225k - $260k/yr
Responsibilities
- Design and maintain distributed training systems for petabyte-scale multimodal robotics datasets across large GPU clusters.
- Identify and resolve bottlenecks in data loading, preprocessing, model computation, and inter-node communication to improve GPU utilization and reduce training time.
- Develop and refine neural network architectures, loss functions, and training strategies for autonomy tasks using high-dimensional sequential sensor data.
- Configure, monitor, and maintain multi-machine, multi-GPU training jobs for stability, fault tolerance, and efficient resource use.
- Build scalable robotics data preprocessing, transformation, and augmentation systems for model training.
- Integrate models, experiments, and new training approaches into production training pipelines with ML scientists and engineers.
- Analyze training metrics, model outputs, and experiment logs to guide improvements in models, data, and training strategies.
- Develop tools and workflows for running experiments, tracking results, and rapidly iterating on model ideas.
Requirements
- Master’s or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a closely related technical discipline.
- At least 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, with distributed training frameworks and large datasets.
- Strong Python programming skills for machine learning models, data pipelines, and training workflows.
- Strong knowledge of neural networks, optimization algorithms, loss functions, model evaluation, and training methodologies.
- Preferred experience resolving compute utilization, memory usage, and data throughput bottlenecks in machine learning systems.
- Preferred experience with robotics or autonomous-driving datasets containing camera video, LiDAR point clouds, radar, or telemetry data.
- Preferred experience developing models that combine images, point clouds, and structured sensor data.
- Peer-reviewed publications or significant research contributions in machine learning, robotics, or related fields are preferred.
Benefits
- Qualified candidates may work remotely in Canada.
- The listed base salary range applies to candidates based in the US; compensation may vary by location, experience, and role alignment.
Tech Stack
Categories
ML EngineeringRobotics