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.

Requirements

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.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningPythondeep learningPyTorchTensorFlowC/C++signal processingdynamic object trackingsensor fusionexploratory data analysis

Soft Skills

collaborationmentoringcommunicationproblem-solvingleadership