Lead Machine Learning Engineer
Serve Robotics · Remote · USA (remote) · Montreal (remote) · British Columbia (remote) · Calgary (remote) · Toronto (remote) · Ottawa (remote) ·
COMPENSATION
$225k – $260k
Listed on the employer's job board (Ashby)
- Lead level role
- Fully remote (Canada)
- Python + Distributed ML
- Robotics autonomy focus
- Petabyte-scale data
Join Serve Robotics to scale machine learning infrastructure for their fleet of autonomous delivery robots. You will design systems that process petabyte-scale multimodal datasets, enabling high-performance autonomy models. The team collaborates closely with researchers to improve training efficiency and model iteration speed.
What you'll do
- Design training systems for petabyte-scale multimodal datasets like video and point clouds
- Resolve bottlenecks in data loading, preprocessing, and inter-node communication
- Refine neural network architectures for high-dimensional sequential sensor data
- Configure and monitor distributed training jobs across multiple GPUs for stability
- Implement scalable systems to preprocess and augment large robotics datasets
- Integrate new models and experiments into the production training pipeline
- Analyze training metrics and logs to guide architecture and strategy improvements
- Develop tools for teams to run experiments and track results efficiently
What you'll need
- Master’s or PhD in Computer Science, Robotics, EE, ML or related field
- Minimum 5 years experience developing and deploying ML models in production
- Hands-on experience training models across multiple GPUs or compute nodes
- Familiarity with distributed training frameworks and large dataset handling
- Strong programming skills in Python for models and data pipelines
- Solid knowledge of neural networks, optimization, and loss functions
Nice to have
- Experience resolving training bottlenecks in compute and memory usage
- Background with robotics or autonomous driving datasets using LiDAR or radar
- Experience combining multiple data modalities into unified learning systems
- Peer-reviewed publications or significant research contributions in ML
Stack
- Python
- Distributed Training Frameworks
- GPU Clusters
- Neural Networks
How you'll work
- Remote work available for candidates based in Canada
- US salary range listed but compensation varies by location