United States onlyAs a Machine Learning Engineer in the AI squad, reporting to the Director of AI Engineering, you’ll focus on developing AI-driven solutions to combat vehicle and content theft. This role is critical for driving innovation in our security technologies, helping to safeguard assets and push the boundaries of what’s possible in the field. If you’re passionate about solving real-world challenges with cutting-edge solutions, this is the opportunity for you.
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.
Requirements
- 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 Qualifications:
- 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.
Benefits
Compensation Range
Compensation may vary depending on skills and experience.
Base Salary: £46,600 - £67,300 / $83,000 - $105,000
Diversity, Equity and Inclusion: At Canopy, we're on a mission to end theft from vehicles and revolutionize vehicle security by building cutting-edge technology. We will achieve this by prioritizing individuals and staying attuned to the evolving needs of our people, users, and industry trends. We foster a workplace culture that embraces diversity and authenticity, enabling us to flourish as a team of exceptional individuals working towards a common purpose. We gain a deeper understanding of our users' experiences by continuously improving our skills and expanding our knowledge. A more diverse, equitable, and inclusive Canopy leads to greater innovation and success.
Equal Opportunity: Canopy does not discriminate on the basis of race, sex, color, religion, age, national origin, marital status, disability, veteran status, genetic information, sexual orientation, gender identity or any other reason prohibited by law in provision of employment opportunities and benefits.
