<div><b>About Ampd Energy</b></div><div><br></div><div>Ampd Energy is transforming how cities build - cleaner, smarter, and more sustainably. We are trailblazers in making the construction industry emission-free. Our flagship product, the Ampd Enertainer, is an advanced, compact, and connected battery energy storage system (BESS) designed to replace the dirty, noisy, and hazardous diesel generators that power construction sites around the world. By combining connected hardware with advanced software and AI, we reduce emissions, noise, and operational complexity.</div><div><br></div><div>We envision an emission-free future across all industries. Our mission is to develop robust, versatile, and easy-to-use products that deliver clean energy - empowering businesses and people to achieve more. We're seeking individuals who bring entrepreneurial drive, adaptability, and enthusiasm, and who thrive in an environment of continuous learning and evolution.</div><div><br></div><div><b style="font-size:11pt;">Role Overview</b><span style="font-size:11pt;">: </span></div><div><br></div><div><span style="font-size:11pt;">Join our mission to revolutionize renewable energy through cutting-edge Battery Energy Storage Systems (BESS). As a Senior Machine Learning Engineer, you’ll lead the development of predictive maintenance models to enhance the reliability and efficiency of our BESS solutions. You’ll be the go-to ML expert on our engineering team, driving innovation by designing algorithms that predict component failures, optimize system performance, and driving the deployment of them. Your work will directly impact the scalability and sustainability of clean energy infrastructure worldwide and help ensure our latest innovations usher us into the future.</span></div><br><br><h3>Responsibilities</h3><ul><li><b>Develop, deploy, and monitor models: </b>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.</li><li><b>Collaborate with data engineers:</b> Identify and help scale necessary data pipelines, both real-time and batch, to facilitate the usage of information for model development and utilization.</li><li><b>Partner with the platform team:</b> Collaborate on the integration of the models in production to work with both edge devices (e.g., IoT systems) and cloud-based inference systems.</li><li><b>Establish robust monitoring:</b> for model performance, including drift detection, degradation, and continuous improvement strategies.</li><div><br></div><div>Example Responsibilities: </div><li><b>Failure prediction</b>: 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.</li><li><b>Cross-Functional Collaboration</b>: Working with field operations and customer success teams to understand knowledge management needs and build practical solutions</li><li><b>Cost savings detection</b>: Create cost optimization model that can help consumers manage energy utilization cycles to best run their business.</li></ul><h3>Qualifications</h3><ul><li>5+ years proven expertise in applied ML engineering, with experience deploying customer-facing models to production</li><li>Strong Python skills with experience in core packages such as NumPy, Pandas, Scikit, XGBoost, TensorFlow, Faust, or similar ML, Deep Learning, and Analytics packages</li><li>Experience going from zero to one in predictive, time-series, and anomaly detection models</li><li>Experience with coordinating disparate data sources and driving ETL requirements </li><li>Excited to work in a startup environment with real operational impact</li></ul><h3>Preferred Qualifications</h3><ul><li>Knowledge of battery or energy storage systems and their degradation patterns (preferred but not required).</li><li>Experience deploying ML models on edge devices (e.g., AWS IoT Greengrass, Azure IoT Edge, or similar).</li><li>Familiarity with streaming data platforms (e.g., Kafka, Apache Spark Streaming, or equivalent).</li></ul><h3>What We Offer</h3><ul><li>Impactful Work: Contribute to the future of clean energy by improving the reliability of next-generation BESS technology.</li><li>Innovative Environment: Work with cutting-edge ML tools and collaborate with a passionate, cross-functional team.</li><li>Growth Opportunities: Access to professional development, mentorship, and opportunities to lead high-impact projects.</li><li>Flexible Culture: Enjoy a hybrid work model, competitive benefits, and a supportive team environment.</li></ul><br><div>Ampd Energy offers comprehensive benefits, professional development opportunities, and a multi-cultural, collaborative, and diverse environment.</div><div><br></div><div>All information provided will be treated in strict confidence and used solely for recruitment purposes. </div><div><br></div><div>Ampd Energy is an equal-opportunity employer. All candidates will be assessed on merit without regard to age, race, gender, sexual orientation, religion, nationality, marital status, political affiliation, or any other factor protected by law.</div>

Ampd Energy is an energy storage company ensuring businesses and brands see the light and stay switched on. Their revolutionary Ampd Silo means power to empower. Co-founded by electric visionaries Brandon Ng and Luca Valente and based in Hong Kong, Ampd designs, engineers and makes state-of-the-art, grid-connected energy storage systems.

Their mission at Ampd Energy is to provide clean affordable and most importantly dependable electricity for the three billion people and millions of enterprises that suffer from unreliable power. Ampd Energy is committed to developing and deploying the latest advances in technology to deliver on their mission. Their state-of-the-art products provide power to empower.

Senior Machine Learning Engineer

<div><b>About Ampd Energy</b></div><div><br></div><div>Ampd Energy is transforming how cities build - cleaner, smarter, and more sustainably. We are trailblazers in making the construction industry emission-free. Our flagship product, the Ampd Enertainer, is an advanced, compact, and connected battery energy storage system (BESS) designed to replace the dirty, noisy, and hazardous diesel generators that power construction sites around the world. By combining connected hardware with advanced software and AI, we reduce emissions, noise, and operational complexity.</div><div><br></div><div>We envision an emission-free future across all industries. Our mission is to develop robust, versatile, and easy-to-use products that deliver clean energy - empowering businesses and people to achieve more. We're seeking individuals who bring entrepreneurial drive, adaptability, and enthusiasm, and who thrive in an environment of continuous learning and evolution.</div><div><br></div><div><b>Role Overview</b><span>: </span></div><div><br></div><div><span>Join our mission to revolutionize renewable energy through cutting-edge Battery Energy Storage Systems (BESS). As a Senior Machine Learning Engineer, you’ll lead the development of predictive maintenance models to enhance the reliability and efficiency of our BESS solutions. You’ll be the go-to ML expert on our engineering team, driving innovation by designing algorithms that predict component failures, optimize system performance, and driving the deployment of them. Your work will directly impact the scalability and sustainability of clean energy infrastructure worldwide and help ensure our latest innovations usher us into the future.</span></div><br><br><h3>Responsibilities</h3><ul><li><b>Develop, deploy, and monitor models: </b>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.</li><li><b>Collaborate with data engineers:</b> Identify and help scale necessary data pipelines, both real-time and batch, to facilitate the usage of information for model development and utilization.</li><li><b>Partner with the platform team:</b> Collaborate on the integration of the models in production to work with both edge devices (e.g., IoT systems) and cloud-based inference systems.</li><li><b>Establish robust monitoring:</b> for model performance, including drift detection, degradation, and continuous improvement strategies.</li><div><br></div><div>Example Responsibilities:&nbsp;</div><li><b>Failure prediction</b>: 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.</li><li><b>Cross-Functional Collaboration</b>: Working with field operations and customer success teams to understand knowledge management needs and build practical solutions</li><li><b>Cost savings detection</b>: Create cost optimization model that can help consumers manage energy utilization cycles to best run their business.</li></ul><h3>Qualifications</h3><ul><li>5+ years proven expertise in applied ML engineering, with experience deploying customer-facing models to production</li><li>Strong Python skills with experience in core packages such as NumPy, Pandas, Scikit, XGBoost, TensorFlow, Faust, or similar ML, Deep Learning, and Analytics packages</li><li>Experience going from zero to one in predictive, time-series, and anomaly detection models</li><li>Experience with coordinating disparate data sources and driving ETL requirements&nbsp;</li><li>Excited to work in a startup environment with real operational impact</li></ul><h3>Preferred Qualifications</h3><ul><li>Knowledge of battery or energy storage systems and their degradation patterns (preferred but not required).</li><li>Experience deploying ML models on edge devices (e.g., AWS IoT Greengrass, Azure IoT Edge, or similar).</li><li>Familiarity with streaming data platforms (e.g., Kafka, Apache Spark Streaming, or equivalent).</li></ul><h3>What We Offer</h3><ul><li>Impactful Work: Contribute to the future of clean energy by improving the reliability of next-generation BESS technology.</li><li>Innovative Environment: Work with cutting-edge ML tools and collaborate with a passionate, cross-functional team.</li><li>Growth Opportunities: Access to professional development, mentorship, and opportunities to lead high-impact projects.</li><li>Flexible Culture: Enjoy a hybrid work model, competitive benefits, and a supportive team environment.</li></ul><br><div>Ampd Energy offers comprehensive benefits, professional development opportunities, and a multi-cultural, collaborative, and diverse environment.</div><div><br></div><div>All information provided will be treated in strict confidence and used solely for recruitment purposes. </div><div><br></div><div>Ampd Energy is an equal-opportunity employer. All candidates will be assessed on merit without regard to age, race, gender, sexual orientation, religion, nationality, marital status, political affiliation, or any other factor protected by law.</div>

Senior Machine Learning Engineer

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Ampd

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