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.
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 creating C/C++ applications utilizing modern language features and build systems, preferably for porting ML inference applications from Python to edge devices.

Benefits

Diversity, Equity and Inclusion initiatives
Equal Opportunity employment practices

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningPythondeep learningPyTorchTensorFlowKalman FiltersSVMsHMMsDecision Treessignal processing