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Senior ML Engineer, Multi-Sensor Modeling
Gridware
San Francisco, USA
Full-time
Senior Level
Hybrid
USD 190000-205000 yearly
Discovered 1 weeks ago
Free
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- Develop algorithms that improve the speed, accuracy, and reliability of Gridware’s automated hazard detection systems
- Work with multimodal time-series and spatial sensor data across diverse sampling rates and noise characteristics.
- Design models that are robust, interpretable, and deployable in production environments.
- Live in the data; help curate & share strategic & well-defined datasets that help solve our highest-value challenges
- Explore advanced approaches such as graph-based learning for grid topology reasoning, geospatial modeling and localization and multimodal fusion across acoustic, magnetic, vibration, electrical, and visual signals
- Write clean, scalable, well-tested Python code that integrates into a large shared codebase.
- Build end-to-end ML pipelines including data processing, feature extraction, training, evaluation, and deployment.
- Optimize models for performance, reliability, and real-world constraints.
- Collaborate on infrastructure for model monitoring, validation, and continuous improvement.
- Translate complex analyses into clear insights for engineers, operators, and leadership.
- Frame solutions to ambiguous, open-ended problems to achieve buy-in from various stakeholders by focusing on the business impact of your projects
- Communicate uncertainty, tradeoffs, and model behavior effectively.
- Partner cross-functionally with software, data engineering, product, and event-reporting teams.
- Help shape technical direction and best practices for ML at Gridware. This includes exemplifying standards for experiment tracking, model versioning, reproducibility, and lifecycle management.
- 5+ years of experience in machine learning, signal processing, or applied physics in production environments.
- Strong programming skills in Python and experience contributing to large, shared codebases.
- Experience working within modern software stacks, including cloud platforms, containerization, and CI/CD workflows
- Excellent written and verbal communication, especially explaining data and models clearly.
- Experience with Graph Neural Networks or learning over physical/topological systems.
- Familiarity with power systems, embedded sensing, or edge ML.
- Proven experience with time-series modeling and noisy real-world sensor data.
- Experience with multimodal learning or sensor fusion.
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