Senior Machine Learning Engineer

About Ampd Energy

Role Overview

Responsibilities

• Develop, deploy, and monitor models:

  Build, launch, and correct predictive, anomaly detection, and time series models that help identify component failure, time to failure, and risks in BESS operations using high-frequency sensor data, historical trends, geographical information, and more.

• Collaborate with data engineers:

  Identify and help scale necessary data pipelines, both real-time and batch, to facilitate the usage of information for model development and utilization.

• Partner with the platform team:

  Collaborate on the integration of the models in production to work with both edge devices (e.g., IoT systems) and cloud-based inference systems.

• Establish robust monitoring:

  for model performance, including drift detection, degradation, and continuous improvement strategies.

• Failure prediction

  : Launch a predictive model that will help customers determine when components within the systems need to be replaced and track if the model has degraded.

• Cross-Functional Collaboration

  : Working with field operations and customer success teams to understand knowledge management needs and build practical solutions

• Cost savings detection

  : Create cost optimization model that can help consumers manage energy utilization cycles to best run their business.

Qualifications

• 5+ years proven expertise in applied ML engineering, with experience deploying customer-facing models to production
• Strong Python skills with experience in core packages such as NumPy, Pandas, Scikit, XGBoost, TensorFlow, Faust, or similar ML, Deep Learning, and Analytics packages
• Experience going from zero to one in predictive, time-series, and anomaly detection models
• Experience with coordinating disparate data sources and driving ETL requirements 
• Excited to work in a startup environment with real operational impact

Preferred Qualifications

• Knowledge of battery or energy storage systems and their degradation patterns (preferred but not required).
• Experience deploying ML models on edge devices (e.g., AWS IoT Greengrass, Azure IoT Edge, or similar).
• Familiarity with streaming data platforms (e.g., Kafka, Apache Spark Streaming, or equivalent).

What We Offer

• Impactful Work: Contribute to the future of clean energy by improving the reliability of next-generation BESS technology.
• Innovative Environment: Work with cutting-edge ML tools and collaborate with a passionate, cross-functional team.
• Growth Opportunities: Access to professional development, mentorship, and opportunities to lead high-impact projects.
• Flexible Culture: Enjoy a hybrid work model, competitive benefits, and a supportive team environment.