Sr. Machine Learning Engineer 140,000 to 180,000 per year Yearly Bonus Summary: As a Senior Machine Learning Engineer within the AI Squad 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. - Apply computer vision and signal processing techniques --- including object detection, semantic segmentation, and activity recognition --- to large-scale sensory datasets (image, audio, radar, accelerometer) to derive insights and guide modeling strategies - Mentor and guide junior engineers and contribute to the hiring process and technical reviews. Requirements: - Bachelor's degree in Computer Science, Data Science, Engineering, or a related field. - 5+ years of professional experience developing and implementing ML for perception systems with expertise in at least one of either radar, audio, camera, or LiDAR. - Strong foundations in computer vision, including object detection, tracking, and semantic segmentation applied to real-world sensor data. - 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. - All candidates must reside in either EST or CST time zones and be authorized to work in the US without sponsorship, now or in the future. Preferred Qualifications: - Experience in deploying models to edge hardware, including experience with PyTorch and ONNX and model compression techniques, e.g. quantization and pruning. - Experience using cloud computing platforms, e.g., AWS or GCP. - Experience with MATLAB for algorithm prototyping and research. - Experience with Docker or containerization. Benefits: Comprehensive medical benefits coverage, dental plans and vision coverage. Health