Senior Machine Learning Engineer

As a Senior Machine Learning Engineer within the AI Squad at Canopy and reporting to the Director of AI Engineering, you’ll contribute to the development of cutting-edge AI solutions to combat vehicle and content theft. In this senior role, you’ll play a pivotal part in shaping our AI roadmap, mentoring junior engineers, and influencing system architecture decisions. This is a high-impact role with visibility across engineering and product leadership.

Responsibilities: 

• Contribute to the design, development, and deployment of robust machine learning models for production use in real-world security applications.
• Develop within the full machine learning lifecycle; from problem definition to data pipeline design, model development, validation, deployment, and monitoring. 
• Establish and refine best practices in our ML system architecture, CI/CD pipelines for ML, and reproducible research methodologies. 
• Collaborate with cross-functional stakeholders including product managers, data engineers, and MLOps teams to ensure seamless model integration and delivery.
• Perform advanced exploratory data analysis on large-scale sensory datasets (image, audio, radar, accelerometer) to derive insights and guide modeling strategies.
• Stay ahead of industry advancements in machine learning, AI sensing, and signal processing, incorporating the latest innovations into Canopy’s technology stack.
• Mentor and guide junior engineers and contribute to the hiring process and technical reviews.

• 5+ years of professional experience developing and implementing ML for perception systems with expertise in at least one of either RADAR, camera, or LiDAR. 
• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
• Expertise in Python with extensive experience in at least one deep learning framework (PyTorch or TensorFlow.
• Proven ability to develop production-grade ML applications for training, evaluation and inference on large-scale datasets. 
• Experience creating C/C++ applications utilizing modern language features and build systems, preferably for porting ML inference applications from Python to edge devices/embedded systems.
• White-box understanding of classical ML algorithms (SVMs, HMMs, Decision Trees) and modern neural network models and architectures (CNNs, transformers) with significant experience applying them for perception systems.
• Experience implementing and applying dynamic object tracking, with experience using sensor fusion as a preference.  
• Proficiency in Unix-based environments (Linux, macOS) including working with remote servers and services, virtual computers and clusters. 
• Proficiency in signal processing techniques such as time/frequency-domain processing (e.g. Fourier Transform), filtering, and noise reduction. 
  

 Preferred Qualifications:

• Experience in deploying models to edge hardware, including experience with PyTorch and ONNX and model compression techniques, e.g. quantisation and pruning.
• Experience using cloud computing platforms, e.g., AWS or GCP.
• Experience with MATLAB for algorithm prototyping and research.
• Experience with Docker or containerisation. 
• Reside within the Detroit area or nearby, with the ability to work in a hybrid environment and regularly commute to our Detroit office as needed.