- Company Name
- Jetson
- Job Title
- Data & ML Engineer
- Job Description
-
**Job Title:** Data & ML Engineer
**Role Summary:**
Design, develop, and maintain end‑to‑end data pipelines and machine‑learning systems that power energy optimization, scheduling, anomaly detection, and forecasting. Ensure reliable data flow, model performance, and seamless integration with backend services and edge devices.
**Expactations:**
- Operate autonomously in ambiguous, data‑heavy environments.
- Balance model accuracy with latency, reliability, and explainability.
- Collaborate cross‑functionally with product, firmware, backend, and design teams.
- Uphold engineering rigor through clean code, testing, and CI/CD practices.
**Key Responsibilities:**
- Build and sustain streaming and batch data pipelines, data models, and warehouses/lakes.
- Perform feature engineering, model training, deployment, monitoring, and iterative improvement.
- Design APIs and contracts between ML services, backend systems, and edge devices, handling versioning and failure modes.
- Implement drift detection, retraining schedules, and robust failure handling for production models.
- Contribute to data architecture, modeling standards, and AI governance frameworks.
**Required Skills:**
- Strong data engineering foundation (SQL/NoSQL, data modeling, ETL, streaming platforms).
- Proven experience delivering production ML models; familiarity with model lifecycle management.
- Proficiency in Python (or comparable language) and ML libraries (e.g., TensorFlow, PyTorch, scikit‑learn).
- Understanding of time‑series data, optimization, or control‑system contexts.
- Solid software engineering practices: version control, unit/integration testing, CI/CD pipelines.
- Excellent communication and teamwork skills across multidisciplinary groups.
**Required Education & Certifications:**
- Bachelor’s degree in Computer Science, Data Science, Electrical Engineering, or a related technical field (or equivalent practical experience).
- Relevant certifications (e.g., AWS Certified Data Analytics, Google Cloud Professional Data Engineer, or ML specialization) are a plus but not mandatory.