Responsibilities
- Design and manage robust, scalable machine learning codebases meeting production standards.
- Lead the strategy for machine learning operations, covering model deployment, monitoring, and lifecycle processes.
- Utilize advanced signal processing methods on one-dimensional sensor inputs such as audio, motion, and motor signals.
- Work directly with clients to collect, troubleshoot, and evaluate data for training and validating models.
- Build and refine machine learning models optimized for time-series and sensor-based applications, using algorithms like SVMs, decision trees, neural networks, and reinforcement learning.
- Adapt and deploy machine learning models onto microcontrollers and embedded hardware platforms.
- Create compact, efficient models designed for edge computing environments.
- Establish and manage continuous integration and continuous deployment pipelines, including GitHub automation and testing systems.
- Develop and sustain comprehensive test suites and technical documentation to ensure software integrity.
- Construct physical test setups with sensors and embedded devices to gather real-world data for model training and validation.
- Guide and support junior engineering staff while contributing to cross-team technical direction.
- Keep up with advancements in artificial intelligence, embedded machine learning, and signal analysis techniques.
Work Arrangement
On-site
Other
- The organization follows a hybrid work model allowing employees to work remotely two days per week, with required office presence from Tuesday to Thursday to support teamwork, innovation, and learning.
- The company handles dual-use technology governed by U.S. export control laws. In some cases, an export license from the U.S. government may be required before transferring technology to certain individuals, at the company's sole discretion.
