Senior Machine Learning Engineer
Canopy Open · verified Oct 22, 2026 At a glance
Senior Machine Learning Engineering role at Canopy. Remote, United States · full-time · $126,000–$180,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build and ship production ML models for security applications, owning work from problem framing through monitoring
- Set up data pipelines, validate models, and keep ML delivery reproducible across the full development cycle
- Improve ML architecture and CI/CD practices, then coordinate with product, data engineering, and MLOps on integration
- Analyze image, audio, radar, and accelerometer data to shape modeling approaches
- Coach junior engineers and take part in technical reviews and hiring
What you bring
- At least five years building perception-focused ML systems, with depth in RADAR, cameras, or LiDAR
- A bachelor's degree in computer science, data science, engineering, or a related discipline
- Strong Python and deep-learning framework experience, plus a record of delivering ML training, evaluation, and inference at scale
- Hands-on C/C++ development and applied knowledge of classical ML, neural networks, object tracking, and signal processing
Who this fits
This senior role suits an engineer who has taken perception models into production and can guide junior teammates. The job is remote in the posting, while its preferred qualifications also call for living near Detroit and commuting to the office regularly for hybrid work.
From the employer
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.
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.
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.
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
Compensation Range
Compensation may vary depending on skills and experience.
Base Salary: $126,000 - $180,000
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.