Canopy Children's Solutions · 4 hours ago
Machine Learning Engineer
Canopy is focused on ending theft from vehicles and revolutionizing vehicle security through innovative technology. As a Machine Learning Engineer, you will develop AI-driven solutions to combat vehicle and content theft, working closely with various teams to integrate machine learning solutions and enhance user experiences.
Child CareHealth CareNon ProfitNursing and Residential Care
Responsibilities
Use machine learning techniques to train, debug, and evaluate models for customer deliveries ranging from quick prototypes to full production-level models
Perform exploratory data analysis on the large sensory datasets (image, audio, radar, accelerometer) we have gathered, to develop greater understanding of the problem domain
Define and improve best practices of ML training, systems development, testing and evaluation
When needed, carry out data collection campaigns using custom tooling for capture and labelling
Work closely with Data and MLOps engineers, and Quality Assurance to improve the quality of our datasets and pipelines
Work with product managers to help integrate the machine learning solutions and deliver on the desired user experience
Qualification
Required
3+ years of professional experience developing and implementing machine learning solutions for perception systems, with expertise in at least one of the following: RADAR, camera, audio, 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) and a proven ability to develop production-grade ML applications for training, evaluation and inference on large-scale datasets
White-box understanding of classical ML algorithms (SVMs, HMMs, Decision Trees) and modern neural network architectures with significant experience applying them for perception systems
Experience implementing and applying Kalman Filters or other tracking algorithms for dynamic object tracking and prediction
Proficiency in Unix-based environments (Linux, macOS) including command-line navigation, shell scripting, and familiarity with common system utilities
Knowledge of basic signal processing techniques such as time/frequency-domain processing (e.g. Fourier Transform), filtering, and noise reduction
Preferred
Experience in deploying models to edge hardware, including experience with PyTorch and ONNX and model compression techniques, e.g. quantisation and pruning
US: 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
UK: Reside within the London area or nearby, with the ability to work in a hybrid environment and regularly commute to our London office as needed
Experience using cloud computing platforms, e.g., AWS or GCP
Experience with MATLAB for algorithm prototyping and research
Experience creating C/C++ applications utilizing modern language features and build systems, preferably for porting ML inference applications from Python to edge devices
Company
Canopy Children's Solutions
Canopy Children’s Solutions has been reliably delivering solutions since 1912.
H1B Sponsorship
Canopy Children's Solutions has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
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Distribution of Different Job Fields Receiving Sponsorship
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Trends of Total Sponsorships
2021 (1)
Funding
Current Stage
Late StageCompany data provided by crunchbase