Backend Engineer - Optimization, Data Analytics, & Inference

Core Responsibilities

• Design, prototype, and productionize optimization models and algorithms to enhance mesh network topology, channel allocation, QOE improvements among others.
• Apply operations research (OR) and mathematical modeling to solve complex problems in resource allocation, scheduling, and network optimization.
• Perform large-scale data summarization, analytics, and statistical inference to extract actionable insights from telemetry and performance data.
• Translate research and mathematical formulations into robust, 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.

• Integrate optimization models using Python libraries such as Gurobi within cloud-based microservices.
• Leverage data analytics tools and frameworks (e.g., SQL, Pandas, NumPy, Spark) for summarization, large-scale processing, and insight generation.
• Implement statistical inference and modeling techniques to uncover trends, correlations, and performance drivers.
• Architect and deploy scalable backend services in AWS/GCP/Azure using Kubernetes and modern DevOps practices.
• Build APIs and backend components that expose optimization, analytics, and inference results to applications and other systems.
• Ensure reliability, performance, and observability of algorithmic and analytics services through strong engineering practices.

Qualifications

• Strong programming skills in Python (Scala, Go, or Java experience a plus).
• Expertise in optimization concepts, including linear, nonlinear, and combinatorial optimization.
• Hands-on experience with optimization libraries (e.g., Pyomo, Gurobi, OR-Tools, CPLEX).
• Experience in data analytics, large-scale data summarization, and statistical inference.
• Proficiency in frameworks for distributed data processing and analysis (e.g., 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 and real-time streaming frameworks (Kafka, Pulsar, or similar).
• Proficiency in SQL and NoSQL databases (PostgreSQL, MongoDB, DynamoDB, etc).
• Experience with cloud platforms (AWS, GCP, or Azure) and container orchestration (Kubernetes, Docker).
• Strong mathematical foundation and analytical problem-solving skills.
• Knowledge of Wi-Fi networking fundamentals is a plus.

• 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 (ChatGPT, Copilot, Cursor, etc).
• Contributions to open-source optimization, analytics, or backend projects.