Lead Machine Learning Engineer

About This Opportunity

We are representing a confidential robotics organization that is scaling autonomous delivery operations across multiple North American cities. Our talent team is seeking a Lead Machine Learning Engineer to architect and optimize large-scale training systems for multimodal robotics data. This remote role offers the opportunity to work on cutting-edge autonomy models while solving complex distributed computing challenges.

Responsibilities

• Design and maintain training systems capable of processing petabyte-scale multimodal datasets including video and point cloud data, ensuring efficient distribution across large GPU clusters
• Identify and resolve pipeline bottlenecks in data loading, preprocessing, model computation, and inter-node communication to maximize GPU utilization and reduce training time
• Develop and refine neural network architectures for autonomy tasks handling high-dimensional and sequential sensor data
• Create and adjust loss functions and training strategies to optimize model learning from complex multimodal inputs
• Configure, monitor, and maintain large-scale distributed training jobs across multiple machines and GPUs with focus on stability, fault tolerance, and resource efficiency
• Implement scalable preprocessing, transformation, and augmentation systems for large robotics datasets
• Collaborate with ML scientists and engineering teams to integrate new models and experiments into production training pipelines
• Analyze training metrics, model outputs, and experiment logs to guide improvements in architecture, data usage, and training strategies
• Develop tooling and workflows enabling rapid experimentation, result tracking, and model iteration

What We're Looking For

Required Qualifications:

• Master's or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or related technical discipline
• Minimum 5 years professional experience developing, training, and deploying machine learning models in production environments
• Hands-on experience with distributed training across multiple GPUs or compute nodes, including familiarity with distributed training frameworks
• Strong Python programming skills for implementing ML models, data pipelines, and training workflows
• Solid foundation in neural networks, optimization algorithms, loss functions, model evaluation, and training methodologies

Preferred Experience:

• Track record identifying and resolving training bottlenecks related to compute utilization, memory usage, and data throughput
• Experience training models on robotics or autonomous driving datasets with multimodal sensor inputs (camera, LiDAR, radar, telemetry)
• Background developing models combining multiple data modalities into unified learning systems
• Peer-reviewed publications or research contributions in machine learning or robotics

How We Work With You

Candidates apply directly through Get A Job.ai, where our recruiting team will conduct an initial screening to understand your background and interests. We then submit qualified candidates to our client for consideration. Please do not attempt to contact the client organization directly, as all communication flows through our recruiting team to ensure a smooth process for everyone involved.

Pay

Pay via Get A Job.ai for Canadian-based candidates: $177,000 - $215,000 CAD annually. Compensation for US-based candidates will be discussed during the screening process.

Equal Employment Opportunity: Get A Job.ai is committed to creating a diverse and inclusive recruiting process. We welcome applications from candidates of all backgrounds and provide equal opportunity throughout our screening and submission process.