Role overview
The AI / ML Engineer will develop and deploy machine learning algorithms that analyze multi-sensor time-series data from vehicles and robotic platforms. This position focuses on building robust models that improve system performance, monitoring, and intelligent behavior, working closely with robotics, controls, and embedded teams.
Responsibilities
- Design and implement ML models for time-series sensor data (e.g., currents, torques, IMUs, joint states, vehicle signals, cameras, GPS).
- Build and maintain data pipelines for collection, preprocessing, feature extraction, and labeling.
- Prototype algorithms in Python (e.g., PyTorch, TensorFlow) and collaborate with embedded engineers to create deployable, resource-efficient models.
- Evaluate model performance using appropriate metrics; iterate to improve robustness and generalization across platforms and use cases.
- Work with robotics and vehicle engineers to understand requirements and convert them into concrete ML problems and model specifications.
- Support data visualization, dashboards, and tools for internal users to interpret model outputs and system behavior.
- Document models, experiments, datasets, and results to ensure reproducibility and traceability.
Basic qualifications
- Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, Applied Mathematics, or a related field.
- Hands-on experience with machine learning for time-series or sensor data.
- Strong proficiency in Python and ML frameworks such as PyTorch or TensorFlow.
- Familiarity with data science tools and workflows (NumPy, Pandas, Jupyter, etc.).
- Ability to work in a cross-functional team and communicate technical concepts clearly.
Preferred qualifications
- Experience with embedded / edge AI or model compression and optimization techniques.
- Familiarity with control systems, robotics, or vehicle dynamics.
- Experience with MLOps tools (experiment tracking, model versioning, CI/CD for ML).
- Prior work in a product or R&D environment with multi-disciplinary teams.
Tags & focus areas
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