<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><span style="font-size:12.902px;">Overview</span></b></div><div><br></div><div><span style="font-size:12.902px;">Join our mission to revolutionize renewable energy through cutting-edge Battery Energy Storage</span></div><div><span style="font-size:12.902px;">Storage Systems (BESS). As a Senior Machine Learning Engineer, you’ll lead the </span></div><div><span style="font-size:12.902px;">development of predictive maintenance models to enhance the reliability and efficiency </span></div><div><span style="font-size:12.902px;">of our BESS solutions. You’ll be the go-to ML expert on our engineering team, driving </span></div><div><span style="font-size:12.902px;">innovation by designing algorithms that predict component failures, optimize system </span></div><div><span style="font-size:12.902px;">performance, and drive the deployment of them. Your work will directly impact the </span></div><div><span style="font-size:12.902px;">scalability and sustainability of clean energy infrastructure worldwide and help ensure </span></div><div><span style="font-size:12.902px;">our latest innovations usher us into the future.</span></div><br><br><h3>Key Responsibilities</h3><ul><li>Develop and deploy predictive models for component failure and time-to-failure estimation using high-frequency sensor data.</li><li>Design advanced anomaly detection and time-series models to identify and mitigate risks in BESS operations.</li><li>Collaborate with data engineers to define and scale data pipelines for real-time and batch processing.</li><li>Partner with the platform team to deploy models on edge devices (e.g., IoT systems) and cloud-based inference systems.</li><li>Establish robust monitoring for model performance, including drift detection, degradation, and continuous improvement strategies.</li><li>Mentor junior software engineers and share expertise to elevate the team’s overall ML capabilities.</li><div><br></div></ul><h3>Required Experience</h3><ul><li>Proven expertise in applied ML engineering, with experience deploying customer-facing models to production. Typically 5+ years, but exceptions are made for significant experience demonstrations.</li><li>Hands-on experience with developing models for anomaly detection techniques (e.g., Isolation Forest, autoencoders, or one-class methods).</li><li>Strong proficiency in time-series modeling and predictive maintenance for industrial or physical systems.</li><li>Proficiency with AWS ML tools (e.g., SageMaker, IoT Core, Lambda) and real-time inference pipelines.</li><li>Ability to collaborate effectively with software and data engineers to build scalable ML infrastructure.</li><li>Strong problem-solving skills and a passion for tackling complex technical challenges.</li><li>Ability to understand the interconnectivity between business objectives and technical constraints, and make thoughtful recommendations on the best course of action </li></ul><h3>Preferred Experience</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><span>Overview</span></b></div><div><br></div><div><span>Join our mission to revolutionize renewable energy through cutting-edge Battery Energy Storage</span></div><div><span>Storage Systems (BESS). As a Senior Machine Learning Engineer, you’ll lead the </span></div><div><span>development of predictive maintenance models to enhance the reliability and efficiency </span></div><div><span>of our BESS solutions. You’ll be the go-to ML expert on our engineering team, driving </span></div><div><span>innovation by designing algorithms that predict component failures, optimize system </span></div><div><span>performance, and drive the deployment of them. Your work will directly impact the </span></div><div><span>scalability and sustainability of clean energy infrastructure worldwide and help ensure </span></div><div><span>our latest innovations usher us into the future.</span></div><br><br><h3>Key Responsibilities</h3><ul><li>Develop and deploy predictive models for component failure and time-to-failure estimation using high-frequency sensor data.</li><li>Design advanced anomaly detection and time-series models to identify and mitigate risks in BESS operations.</li><li>Collaborate with data engineers to define and scale data pipelines for real-time and batch processing.</li><li>Partner with the platform team to deploy models on edge devices (e.g., IoT systems) and cloud-based inference systems.</li><li>Establish robust monitoring for model performance, including drift detection, degradation, and continuous improvement strategies.</li><li>Mentor junior software engineers and share expertise to elevate the team’s overall ML capabilities.</li><div><br></div></ul><h3>Required Experience</h3><ul><li>Proven expertise in applied ML engineering, with experience deploying customer-facing models to production. Typically 5+ years, but exceptions are made for significant experience demonstrations.</li><li>Hands-on experience with developing models for anomaly detection techniques (e.g., Isolation Forest, autoencoders, or one-class methods).</li><li>Strong proficiency in time-series modeling and predictive maintenance for industrial or physical systems.</li><li>Proficiency with AWS ML tools (e.g., SageMaker, IoT Core, Lambda) and real-time inference pipelines.</li><li>Ability to collaborate effectively with software and data engineers to build scalable ML infrastructure.</li><li>Strong problem-solving skills and a passion for tackling complex technical challenges.</li><li>Ability to understand the interconnectivity between business objectives and technical constraints, and make thoughtful recommendations on the best course of action </li></ul><h3>Preferred Experience</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 EngineerNew

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Ampd

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