• Design, deploy, and maintain Kubernetes infrastructure supporting AI/ML workloads • Manage containerized services, autoscaling, networking, and resource optimization • Design and build high-performance Python APIs and services using FastAPI or similar frameworks • Architect backend systems for scalability, reliability, and low latency • Build integrations between AI/ML systems and the broader Albert platform • Build and operate distributed systems that handle compute-intensive and high-throughput workloads • Design for fault tolerance, graceful degradation, and horizontal scalability • Implement async workflows, job queues, and task orchestration as needed • Architect and maintain data pipelines and storage systems supporting AI/ML workflows • Implement observability including logging, metrics, tracing, and alerting • Own system reliability—troubleshoot issues, conduct post-mortems, and continuously improve • Design CI/CD pipelines and promote automation best practices • Partner closely with ML engineers to understand requirements and deliver production-ready infrastructure • Translate ML prototypes and research code into scalable, maintainable systems

• Lead the development and implementation of advanced algorithms for robotic perception systems tailored to industrial welding tasks, integrating data from diverse vision sensors such as RGB/GigE, LiDAR, and ToF depth sensors. • Oversee research initiatives to address complex welding-related challenges, utilizing image processing, point cloud data, and 3D sensor fusion, contributing to innovative solutions for domain-specific problems. • Collaborate with multidisciplinary teams to design and lead experiments evaluating state-of-the-art deep learning models, optimizing machine learning systems for robotic perception in welding. • Stay at the forefront of advancements in Robotics, Computer Vision, and ML research, driving the integration of cutting-edge technologies into real-world applications, and ensuring these innovations have a high impact on production systems. • Mentor and guide junior engineers, providing technical leadership and fostering collaboration to enhance team expertise in perception systems and machine learning. • Contribute to strategic decisions about system architecture and the direction of robotics perception technologies within the company, ensuring alignment with product and business goals.

• Design and implement machine learning systems to predict future ad inventory,demand, and performance • Research and apply best practices for driving automation with respect to human review processes • Partner with multiple teams to shape and enhance shared systems and pipelines • Come up with creative ways to apply AI tools to develop innovative solutions • Collaborate with and lead backend engineers, data scientists, data engineers, and product managers to establish baselines, inform product decisions, and develop new technologies

• Own the end-to-end machine learning platform, including model development workflows, training, deployment, monitoring, retraining, and lifecycle management. • Define and execute the roadmap for scalable ML infrastructure supporting both real-time and batch use cases. • Lead applied machine learning initiatives supporting compliance and customer success, including areas such as: • Compliance monitoring, alerting, and investigation support • Customer success automation, prioritization, and insight generation • Internal operational tooling and responsible AI adoption • Partner with cross-functional stakeholders to translate complex business, customer, and regulatory problems into production ML solutions. • Build, mentor, and scale a small team of ML engineers and applied scientists, operating as a player-coach when needed.