As a Senior Machine Learning Engineer within the AI Squad at Canopy, reporting to the Director of AI Engineering, you'll contribute to the development of cutting-edge AI solutions to combat vehicle and content theft.
Play a pivotal part in shaping the AI roadmap, mentoring junior engineers, and influencing system architecture decisions.
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 ML system architecture, CI/CD pipelines for ML, and reproducible research methodologies
Collaborate with 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)
Stay ahead of industry advancements in machine learning, AI sensing, and signal processing
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 RADAR, camera, or LiDAR
Bachelor's degree in Computer Science, Data Science, Engineering, or a related field
Expertise in Python with extensive experience in 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, 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 (CNNs, transformers)
Experience implementing and applying dynamic object tracking, with sensor fusion as a preference
Proficiency in Unix-based environments (Linux, macOS) including remote servers and clusters
Proficiency in signal processing techniques such as time/frequency-domain processing (Fourier Transform), filtering, and noise reduction
Preferred qualifications
Experience deploying models to edge hardware, including PyTorch and ONNX and model compression techniques (quantisation, pruning)
Experience using cloud computing platforms (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
Benefits
Comprehensive medical benefits coverage, dental plans and vision coverage
Health care and dependent care spending accounts
Employee and Family Assistance Program (EAP)
Employee discount programs
Retirement plan with a generous company match
Generous Paid Time Off, Sick, and Holidays
Family Leave (Maternity, Paternity)
Short- and long-term disability
Life insurance and accidental death & dismemberment insurance