Job
Description
Role Overview: At Plume, you will be working as a Backend Engineer focusing on Optimization, Data Analytics & Inference. Your main responsibility will be to design and implement intelligent solutions that optimize mesh Wi-Fi networks in home environments. This will involve operating at the intersection of backend systems, applied optimization, large-scale data analytics, and statistical inference. Your goal will be to develop algorithms, models, and insights that enhance network performance, efficiency, and reliability for millions of connected devices. Core Responsibilities: - Design, prototype, and productionize optimization models and algorithms to enhance mesh network topology, channel allocation, and QOE improvements. - Apply operations research (OR) and mathematical modeling to solve complex problems related to resource allocation, scheduling, and network optimization. - Perform large-scale data summarization, analytics, and statistical inference to derive actionable insights from telemetry and performance data. - Translate research and mathematical formulations into scalable backend services running in the cloud. - Use simulation and statistical analysis to validate algorithmic performance and impact. - Collaborate with data scientists, network engineers, and platform teams to operationalize optimization and analytics models in production environments. Qualifications: - Strong programming skills in Python (experience in Scala, Go, or Java is a plus). - Expertise in optimization concepts, including linear, nonlinear, and combinatorial optimization. - Hands-on experience with optimization libraries such as Pyomo, Gurobi, OR-Tools, and CPLEX. - Experience in data analytics, large-scale data summarization, and statistical inference. - Proficiency in frameworks for distributed data processing and analysis like Spark, Dask, or Ray. - Background in operations research, applied mathematics, or algorithms. - Solid understanding of distributed systems, microservices, and event-driven architectures. - Familiarity with data engineering, real-time streaming frameworks (e.g., Kafka, Pulsar), SQL, NoSQL databases (PostgreSQL, MongoDB, DynamoDB), cloud platforms (AWS, GCP, Azure), and container orchestration (Kubernetes, Docker). - Strong mathematical foundation and analytical problem-solving skills. - Knowledge of Wi-Fi networking fundamentals is a plus. Nice To Have: - Experience optimizing wireless or mesh networks or other complex distributed systems. - Background in functional programming (Scala preferred). - Experience building APIs and backend services around optimization, analytics, or inference logic. - Familiarity with AI-assisted developer tools like ChatGPT, Copilot, Cursor. - Contributions to open-source optimization, analytics, or backend projects. Role Overview: At Plume, you will be working as a Backend Engineer focusing on Optimization, Data Analytics & Inference. Your main responsibility will be to design and implement intelligent solutions that optimize mesh Wi-Fi networks in home environments. This will involve operating at the intersection of backend systems, applied optimization, large-scale data analytics, and statistical inference. Your goal will be to develop algorithms, models, and insights that enhance network performance, efficiency, and reliability for millions of connected devices. Core Responsibilities: - Design, prototype, and productionize optimization models and algorithms to enhance mesh network topology, channel allocation, and QOE improvements. - Apply operations research (OR) and mathematical modeling to solve complex problems related to resource allocation, scheduling, and network optimization. - Perform large-scale data summarization, analytics, and statistical inference to derive actionable insights from telemetry and performance data. - Translate research and mathematical formulations into scalable backend services running in the cloud. - Use simulation and statistical analysis to validate algorithmic performance and impact. - Collaborate with data scientists, network engineers, and platform teams to operationalize optimization and analytics models in production environments. Qualifications: - Strong programming skills in Python (experience in Scala, Go, or Java is a plus). - Expertise in optimization concepts, including linear, nonlinear, and combinatorial optimization. - Hands-on experience with optimization libraries such as Pyomo, Gurobi, OR-Tools, and CPLEX. - Experience in data analytics, large-scale data summarization, and statistical inference. - Proficiency in frameworks for distributed data processing and analysis like Spark, Dask, or Ray. - Background in operations research, applied mathematics, or algorithms. - Solid understanding of distributed systems, microservices, and event-driven architectures. - Familiarity with data engineering, real-time streaming frameworks (e.g., Kafka, Pulsar), SQL, NoSQL databases (PostgreSQL, MongoDB, DynamoDB), cloud platforms
